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Remote sensing technology in the new era gradually need a new scientific paradigm driven by big data and knowledge. However, in the current research, the use of existing knowledge is slightly insufficient. As for the whole research process, there is a lack of guidance based on the field knowledge. In view of the above problems in the field of remote sensing in agriculture, it is of great significance to organize knowledge based on concept graph and develop knowledge-driven big data analysis theory and methods. This paper focuses on the construction and application of agricultural remote sensing concept graph. Firstly, it explores the core contents, research hotspots and development trends of the current agricultural remote sensing field through reference visualization and analysis methods such as keyword frequency statistics, keyword co-occurrence, word detection with stronger citation burst and so on. Then, this paper summarizes the comprehensive ontology of agricultural remote sensing based on the results of keyword clustering. Thus, the schema layer of concept graph is constructed from top to bottom. Under the guidance of the schema layer, the data layer of the concept graph is artificially filled from the bottom up with different triads stored in Neo4j. In terms of application, retrieval, query and recommendation of the proposed concept graph for agricultural remote sensing tasks is a typical application case. Based on the results of the recommendation, it could guide the process of information extraction such as cultivated land parcel delineation and crop type identification at the parcel scale. The study of concept graph helps to summarize and generalize the relevant knowledge in the field of agricultural remote sensing. Relevant research provide effective solutions for automated and intelligent agricultural information extraction and application. [ABSTRACT FROM AUTHOR]

Potash plays an important role in agriculture and food security, so does the estimation of the content of substances in potash. Chemical methods for such estimation tasks are time-consuming, whereas thermal infrared hyperspectral (TIH) images provide us with an opportunity to do estimation efficiently. This paper hence applies the TIH technology to predict the content of various substances in potash. On the one hand, the TIH technology provides rich hyperspectral information for substance discovery and analysis. On the other hand, it introduces challenges of dimensionality and information redundancy, leading to potential decreased predicting accuracy. To solve the problem, as well as easing the burden of detection instruments in terms of band usage, band selection is critical. This paper proposes a dynamic metric-guided particle swarm optimization (DM-GPSO) method for band selection. It adaptively adjusts parameters to prevent local optima, avoiding the need for manual parameter tuning. Variety of band selection algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO), and improved PSO algorithms, are tested to extract and analyse the characteristic bands of potash. Experimental results demonstrate that the TIH technology combined with a proper band selection methodis effective in estimating the content of potash. The new DM-GPSO can select different combinations of bands, which lead to better and more stable prediction results. The analysis on the combinations of selected bands also reveals that picromerite and potassium chloride have similar intervals of characteristic bands, which can be used to guide the production process of potash in practice. [ABSTRACT FROM AUTHOR]

In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Using high-resolution remote sensing images to accurately obtain PV information over a large region, including location and size, has the advantages of high statistical efficiency and timely data update for the PV energy management. Due to the intra-class diversity of PV panels and the intricate variability in their deployment environments, existing semantic segmentation methods often have problems such as under-segmentation and mis-segmentation. To alleviate these problems, this paper proposes an improved DeepLabv3+ semantic segmentation network to more accurately extract PV panels from high-resolution remote sensing images. With the aim of alleviating under-segmentation, a multi-level context aggregation module is developed. This module can enhance the model's ability to learn the characteristics of PV panels and their surrounding environment by aggregating rich contextual information from multi-scale and semantic levels. To alleviate the problem of mis-segmentation, a hybrid attention module is introduced. This module sequentially and adaptively adjusts the weight distribution in both the channel and spatial dimensions, thus enabling the model to focus more on the feature information and spatial positions of PV objects. Experiments conducted on a self-constructed Beijing PV segmentation dataset show that the method in this paper has advantages of completeness and accuracy in extracting PV panels compared to the baseline model and current mainstream semantic segmentation network. In addition, the results of experiments on extracting PV panels in real region show that our model also has good stability and generalization capability. [ABSTRACT FROM AUTHOR]

In recent years, low-rank sparse representation (LRR) schemes have been applied to the field of hyperspectral image (HSI) anomaly detection (AD) and received attention. However, it is often accompanied by a small amount of noise in the AD, which leads to a high false alarm rate of detection results. In this paper, the HSI-AD algorithm is proposed to address this problem. First, the HSI is unmixed using the deep network to obtain the endmembers and abundance, and the abundance matrix is fused with the HSI to obtain the mixed data, which to a certain extent complements the spatial information of the HSI because the abundance matrix can reflect the subpixel-level information of the target image elements. Then, the mixed data are decomposed based on the model proposed in this paper. To overcome the situation that the sparse part of the traditional model is prone to mix with a small amount of noise, the sparse decomposition of the mixed data into anomaly and noise is used to remove the small amount of noise from the sparse part. A joint dictionary consisting of background and anomaly is used in the above model. In addition, to address the problem of information loss caused by the uniform accumulation of kernel norm over all singular values in the process of model decomposition, this paper designs the γ norm decomposition method to replace the general kernel norm decomposition for a more effective extraction of useful information. [ABSTRACT FROM AUTHOR]

Point cloud registration is the process of aligning and merging multiple point clouds into a same coordinate system. It has many applications in computer vision, robotics, 3D reconstruction, augmented reality, and autonomous driving. Despite its wide-ranging utility, traditional algorithms grapple with challenges such as substantial noise, outliers, limited overlap, motion distortions, and handling large scenes. These complexities impede the effective deployment of point cloud registration in practical real-world scenarios. This paper conducts a comprehensive inquiry into the domain of deep learning-based point cloud registration. It synthesizes recent advancements in algorithms about point cloud registration grounded in deep learning. The exposition delineates insights from four distinct perspectives: multi-layer perceptron, deep neural network, graph convolutional network, and attention mechanism. Furthermore, the paper systematically reviews prevalent datasets and evaluation metrics associated with deep learning-based point cloud registration. A discussion on the existing challenges in point cloud registration and a forward-looking exploration of potential research directions are included. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) assessment methods relying on physical and empirical models are considered to be the most commonly used method at present, but their estimation efficiency and accuracy are deficient. Although the hybrid model of these two methods can address these issues, a poor coupling mechanism can easily occur. Given that, a PROSAIL model coupling particle swarm optimization (PSO) neural network (NN) algorithm (PSO-NN-PROSAIL model) was introduced to invert the winter wheat LAI (WWLAI) at five distinct growth stages. The Xiangfu District in the east of Kaifeng City, Henan Province was served as the study region. Based on the measured WWLAI data at varying stages and GF-1 WFV satellite images, the initial analysis focused on assessing the PROSAIL model's sensitivity in simulating vegetation canopy reflectance. It then calculated six vegetation index models according to the wavelength reflectance of GF-1 WFV and analysed their correlation with LAI to select the input parameters that could be used in the model. Normalized differential vegetation Index (NDVI) and ratio vegetation Index (RVI) as well as vegetation canopy reflectance were employed as input parameters to invert the WWLAI by adopting the PSO-NN-PROSAIL model. The experimental results showed the following: (1) In the vegetation index model, the determination coefficient (R2) of NDVI and RVI was greater than 0.68, implying that NDVI and RVI might serve as input factors for the proposed model in this paper; (2) LAI and chlorophyll a + b content (Cab) were most sensitive to the PROSAIL model in near-infrared and visible light bands; and (3) the PSO-NN-PROSAIL model possessed better LAI inversion accuracy. In summary, the model proposed in this paper provided a technical reference for rapid and accurate remote sensing monitoring of WWLAI. The optimization technology of particle swarm optimization algorithm is integrated into the neural network, and the weight of the neural network is adjusted by its fitness function transformation and inertia weight. By integrating prior knowledge into the inversion of crop leaf area index constructed by machine learning and radiative transfer model, the ill-posed problem of physical model inversion has been improved. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-based approaches are notable for their ability to establish feature associations without relying on physical constraints, unlike traditional strategies that are complex and dependent on expert experience. However, three main challenges hinder the versatility of semantic segmentation models. First, the targets in these images are dense and exist at varying spatial scales, which imposes higher demands on the model for accurate segmentation across scales. Second, the segmentation of small targets in the images is often overlooked, leading to a compromise between fine segmentation and model efficiency. Lastly, the data-intensive nature of remote sensing images and the resource-intensive operations of large-scale networks impose significant communication and computation burdens on edge devices, which may not have sufficient resources to handle them effectively. To address these challenges, this paper proposes a lightweight semantic segmentation method for remote sensing images to achieve high-precision segmentation for multi-scale targets while maintaining low computational complexity. The main components include: (1) embedding the inverted residual block structure to minimize the number of model parameters and computational costs; (2) introducing the parallel irregular space pyramid pooling module to efficiently aggregate multi-scale contextual information for fine-grained recognition of small targets; and (3) embedding transfer learning into the encoder-decoder structure to speed up the convergence rate and improve multi-scale feature fusion capability, thereby reducing semantic information loss. The proposed lightweight method has been extensively tested on real-world high-resolution remote sensing datasets. It achieved PA, MPA, MIoU, and FWIoU scores of 87.90%, 75.76%, 66.29%, and 78.81% on the Vaihingen dataset; 87.03%, 85.31%, 74.85%, and 77.54% on the Potsdam dataset; and 95.37%, 83.33%, 75.70%, and 91.31% on the Aeroscapes dataset. Compared to other popular semantic segmentation models, the proposed method achieved the highest values in all four evaluation indicators, demonstrating its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Recently, U-Net architecture has been extensively explored for remote sensing (RS) image haze removal, demonstrating remarkable performance. However, most existing RS image haze removal methods based on the U-Net fail to fully utilize feature information extracted in the encoding layers during decoding, resulting in unsatisfactory haze removal results. Moreover, most haze removal algorithms neglect the interaction of cross-level feature information, which is particularly important for scene recovery and context information constraints. To address these issues, this paper presents an Embedded U-shaped Network with Cross-Hierarchical Feature Adaptation Fusion Network (EUCHA). Specifically, an Embedded U-shaped Network Framework (EUNF) is proposed to fuse the features extracted from different scale encoding layers as supplementary information into the corresponding decoding layers by embedding multiple parallel U-shaped subnetworks, which alleviates feature information dilution during the decoding process and enhances the network receptive field. Furthermore, we propose a Cross-Hierarchical Feature Adaptive Fusion (CHAF) module, which effectively and adaptively fuses multiple adjacent hierarchical features extracted by channel attention and pixel attention through learnable factors to enhance the expressive ability of features. Experiments were conducted on both real and synthetic datasets and compared with the nine most advanced algorithms. The results showed that EUCHA algorithm performs better in removing artefacts in both RS images and natural images. The source code is available at . [ABSTRACT FROM AUTHOR]

Feature extraction is a core aspect in hyperspectral image classification, which can extract key information closely related to ground cover from complex scene, thus improving classification accuracy. Therefore, designing an effective feature extraction network is a hotspot and a challenge in the current research. In this paper, a feature extraction framework based on 3-D homogeneous attribute decomposition (3D-HAD) is proposed for HSI classification, which consists of the following key technologies. First, the principal component analysis algorithm is applied to the raw HSI to extract the principal components (PCs), and the raw HSI is clustered into many 3D superpixel blocks according to the first three PCs-based over-segmentation strategy. Then, a superpixel intrinsic attribute decomposition (SIAD) is designed to capture reflectance feature and suppress shading feature. Meanwhile, a metric entropy is introduced into the decomposition process to overcome the spectral-spatial weak assumption among pixels. Next, superpixel-guided recursive filtering is employed to preserve global details of HSI to enhance accuracy in HSI classification. Finally, the support vector machine classifier is used to obtain classification results of HSI. Experiments performed on several real hyperspectral data sets with limited training samples indicate that the proposed 3D-HAD method outperforms the classic, advance, and deep learning classification methods. [ABSTRACT FROM AUTHOR]

Building façade parsing is to recognize the building façade image into different categories of individuals including walls, doors, windows, balconies, etc. However, obstructions such as trees present a significant challenge to conducting façade parsing. In this paper, we designed OccFaçade to achieve high-precision parsing of occluded building façades in large urban scenes. OccFaçade primarily incorporates two modules, Multi-layer Dilated Convolution Module (MD-Module) and Multi-scale Row-Column Convolution Module (MRC-Module), to capture repeated texture in local and row-column directions. This aims to leverage repetitive textures to address occlusion challenges in building façade parsing. Besides, we introduce our building façade dataset MeshFaçade from the Mesh data generated by drone imagery to study the occlusion problem of missing textures. The experimental results demonstrate that OccFaçade achieves state-of-the-art performance with mIOU of 85.01%, 84.09%, 72.95%, and 88.83% on the ENPC2014 dataset, ECP dataset, RueMonge2014 dataset, and our MeshFaçade dataset, respectively. The code and data are available at . [ABSTRACT FROM AUTHOR]

Omni-dimensional dynamic convolution can adjust spatial sizes, the amount of input/output channels, and kernel size based on the characteristics of the input, thus improving the capacity to extract characteristics through convolution, resulting in increased computation and model complexity. As a result, it is challenging to deploy these models on resource-limited airborne high-spectral testing systems. This paper proposes a lightweight omni-dimensional dynamic convolution network as a possible solution to this issue. In this network, the LS2CM module is used for spatial-spectral information extraction, and the pyramidal residual network is the basic architecture for extracting features from shallow to deep layers, and lightweight omni-dimensional dynamic convolution is adopted in the convolution module. Overcoming the pyramidal structure can only extract local information, we proposed a new simple spatial-spectral self-attention mechanism to extract global information from hyperspectral. Besides, the Huber function as an activation function. The IP, WHU-Hi-LongKou, SA, and PU datasets were used to verify the accuracy of the suggested network model. The results show that the proposed lightweight network maintains an appropriate equilibrium among model complexity as well as accuracy. [ABSTRACT FROM AUTHOR]

This paper mainly addresses the lack of labelled data and insufficient data utilization in PolSAR image classification. We propose a channel adaptive Complex-Valued Fully Convolutional Networks based on Transfer learning (TF-CVFCN). To solve the problem of less labelled data, we borrowed from the transfer learning method, which can use a large amount of easily obtained relevant or even irrelevant data for pre-training and then use a small amount of source data for network training fine-tuning. Since PolSAR data can obtain different data information by using different decomposition methods, the current research trend is to process PolSAR data in the complex-valued containing more information. In our work, to cater to the proposed network model and make the network design more liberal, a new transfer learning method is proposed, which uses a new method to realize partial layer parameter transfer, and achieves the purpose of optimizing the traditional Transfer learning method. On this basis, we optimize the network structure to improve the classification accuracy of PolSAR data, and evaluate its performance on three datasets. Furthermore, we lighten the TF-CVFCN and optimize the relevant parameters using Competitive Multi-Objective Particle Swarm Optimizer, emphasizing the importance of this parameter optimization in large networks. [ABSTRACT FROM AUTHOR]

The utilization of Unmanned Aerial Vehicles (UAV) in remote sensing (RS) has witnessed a significant surge, offering valuable insights into Earth dynamics and human activities. However, this has led to a substantial increase in the volume of video data, rendering manual screening and analysis impractical. Consequently, there is a pressing need for the development of automated interpretation models for these aerial videos. In this paper, we propose a novel approach that leverages visual dialogue to enhance aerial video captioning. Our model adopts an encoder-decoder architecture, integrating a Visual Question Answering (VQA) task before the captioning task. The VQA task aims to enrich the captioning process by soliciting additional information about the image content. Specifically, our video encoder utilizes ViT-L/16, while the decoder employs Generative Pre-trained Transformer-2 (Distill-GPT-2). To validate our model, we introduce a novel benchmark dataset named CapERA-VQA, comprising videos accompanied by sets of questions, answers, and captions. Through experimental validation, we demonstrate the effectiveness of our proposed approach in enhancing the automated captioning of aerial videos. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a key variable in the study of the global climate system and one of the important parameters in the process of air–sea interaction. Therefore, the demand for SST is developing towards high quality and high precision. In this paper, the ability of the multi-layer perceptron (MLP) optimized by the Aquila optimizer (AO) to solve nonlinear problems and the advantages of the deep belief network (DBN) to effectively process complex data are used to construct the IMLP-DBN inversion algorithm. The algorithm takes into account the influences of atmospheric conditions and satellite zenith angles. The data set is the infrared remote sensing data of the moderate resolution imaging spectroradiometer (MODIS) and the actual measurement data of buoys on sunny days and few clouds. Analysis of the inversion results shows that the root mean square error (RMSE) of the inversion value and the measured value is 0.14, and the sum of square errors (SSE) is 0.78. Compared with the MOD28 product data, the RMSE and SSE of the inversion are reduced by 66% and 24%, respectively. [ABSTRACT FROM AUTHOR]

This paper describes the first assessment of ISRO's indigenously developed S-band Polarimetric Doppler Weather Radar (DWR) at Sriharikota High Altitude Range (SHAR), Sriharikota (13.66°N, 80.23°E). The assessment is done during the passage of a tropical cyclone 'Phethai' and four different cases of Mesoscale convective systems. A comparison of reflectivity measured from SHAR-DWR with India Meteorological Department (IMD) DWR located at Chennai (13.07°N, 80.28°E) and Ku-band space-borne radar of the Global Precipitation Mission (GPM) is done using the common volume matching method. A good comparison is observed between SHAR-DWR with IMD-DWR and GPM-Ku radar in both space and height. The vertical structure of the cyclone is provided using Contour Frequency by Altitude Diagrams (CFAD) which show the vertical extent up to an altitude of 16 km with a reflectivity between 25 and 30 dBZ. Maximum occurrence of 30 dBZ occurs below 6 km. Bright band signatures show ~ 25 dBZ between 4 and 5 km depicting the stratiform precipitation of the cyclone. Further rain rate is estimated using both reflectivity and the polarimetric product which compared well with rain estimated from IMD-DWR and rainfall observed from rain gauges. Rain rate is observed from 5 to 35 mm hr−1. Hydrometer classification during the passage of tropical cyclone 'Phethai' shows the presence of rain below 4 km and graupels, ice crystals, and large droplets above. [ABSTRACT FROM AUTHOR]

The Remote Sensing Image Change Captioning (RSICC) has recently emerged in the field of remote sensing image interpretation; it aims to automatically predict natural language captions of significant semantic changes in bi-temporal remote sensing images. Recent studies of RSICC have improved the accuracy of change captions of bi-temporal remote sensing images to a large extent. Nevertheless, there still remain challenges in multi-scale perception of ground objects and feature enhancement of bi-temporal remote sensing images. To address these challenges and further improve the accuracy of RSICC, a novel deep learning–based end-to-end scale-wised feature enhancement network (SFEN) is proposed in this paper. SFEN integrates four efficient blocks: 1) the siamese backbone network (SBN) to extract initial features of bi-temporal remote sensing images, 2) the siamese receptive field fusion (SRFF) block to explicitly capture multi-scale semantic information of ground objects in bi-temporal feature maps, 3) the siamese global feature enhancement (SGFE) block to adaptively enhance key information and filtering redundant features of bi-temporal feature maps in both channel and spatial dimensions, 4) the change caption decoder (CCD) to map bi-temporal feature maps into natural language. The SFEN aims to precisely capture significant semantic information of ground objects in bi-temporal remote sensing images and predict accurate change captions. Experimental results on LEVIR-CC dataset demonstrate our SFEN outperforms recent state-of-the-art (SOTA) approach in RSICC by 5.2% on CIDEr-D and achieves a new SOTA. [ABSTRACT FROM AUTHOR]

Ultra-wideband antennas that operate at low frequencies usually have a large geometry, which makes them very limited in practical engineering application environment where space is often constrained. In this paper, a novel low-frequency low-profile ultra-wideband magneto-electric (ME) dipole antenna is designed for the application in ground penetrating radar (GPR) to detect deeply buried targets. The antenna is designed at the operation frequency of 230 MHz, so that the objects that are buried as deep as 1.5 m can be detected. Compared to the traditional low-frequency _antennas, it has a profile as low as 740 mm $ \times $ × 140 mm $ \times $ × 150 mm ($0.49\lambda \times 0.09\lambda \times 0.10\lambda $ 0.49 λ × 0.09 λ × 0.10 λ). Based on the simulation analysis and validation measurements, the proposed ME dipole antenna has a wide impedance bandwidth for ${S_{11}} \lt - 10{\rm{dB}}$ S 11 < − 10 dB from 135 MHz to 382 MHz even when environmental influence is considered. The antenna is further validated with a GPR experiment, where a pipeline with a diameter of about 200 mm at 1200 mm beneath the road surface is successfully detected by an impulse GPR system where the proposed antennas are integrated. [ABSTRACT FROM AUTHOR]

Coal is a prominent energy resource in China. Therefore, it is crucial to accurately extract the coal-covered areas. Taking Xinjiang's Zhundong mining region as the study area, Landsat 8 OLI data were employed to assess the spectral curves of four typical ground objects, including vegetation, bare land, water body and bare coal. The Normalized Difference Bare Coal Index (NDBCI) and the Normalized Difference Coal Index (NDCI) were developed to extract coal-covered area. Higher-resolution Sentinel-2B data for the same period were used for verification, with extraction accuracy evaluated by five metrics including Kappa coefficient, Overall accuracy, Checking accuracy, Checking completeness and F1-score. The results of extracting coal-covered areas showed that (1) the NDBCI showed 'internal fragmentation' and the NDCI demonstrated 'pixel overflow' during the extraction process. Therefore, we determined the optimal thresholds −0.03 for NDBCI and 0.04 for NDCI. (2) NDBCI distinguished pixels with lower grey-scale values, such as water body, road and gangue. However, some dump zones and shed patches were misclassified. (3) NDCI generated clear boundaries and more complete interiors, and the dump zone and shed could be distinguished. However, some water body parts were misclassified as coal-covered areas. (4) Combined application of NDBCI and NDCI generated a 'complementary' effect better than both individual modes. Kappa coefficient, Overall accuracy and F1-score reached 0.95, 98.76% and 0.75, respectively. This study successfully extracted coal-covered areas by developing a remote sensing index based on spectral traits and a priori knowledge of the study area. The proposed combined extraction mode achieved high accuracy for rapid and reliable identification of coal-covered areas. Highlights: Two new remote sensing indexes for coal-covered area extraction were proposed in this paper. Complementary advantages were found between NDBCI and NDCI in the extraction. Accuracy assessment was performed based on manually deciphered high-precision raster data. [ABSTRACT FROM AUTHOR]

Air pollution affects not only the air in cities but also extends to all indoor environments (homes, offices, schools, public places, transportation, etc.), where we spend between 80% and 90% of our time. Both indoor and outdoor air quality have emerged as significant health concerns and are integral to national strategies implemented by health and environmental institutes in each country. Recently, complaints regarding outdoor air quality have risen in cities, primarily due to automobile traffic and industrial activities in urban areas, and also indoors within homes, offices, and schools. The following paper presents a methodology for the calibration of low-cost monitoring stations based on measurements in a couple of cities in Colombia as part of the development of a project to reduce the environmental awareness gap in urban areas for the estimation of the air quality through low-cost, flexible, modular, and mobile air quality monitoring station design that could be used to assess air pollution in different indoor and outdoor environments. With the implementation of the low-cost stations, we have calibrated and evaluated the performance of the stations using usual linear regression methods, but we have also explored the use of unsupervised estimation with the help of machine learning algorithms, specifically with Random Forest estimators. We have found a significant improvement with using Random Forest for station calibration compared with those found using simple linear regressions for calibration effects. We have found that all the models offer a significant improvement in terms of RMSE. The regression model improves RMSE by up to 70%, while the multiple regression model does so by up to 73%. However, it is the Random Forest that shows the most remarkable improvement, with a reduction in RMSE of up to 86%. [ABSTRACT FROM AUTHOR]

High-resolution wide-swath (HRWS) imaging has emerged as a focal point in synthetic aperture radar (SAR) research. However, conventional SAR systems face challenges in achieving both high azimuth resolution and wide swath simultaneously due to the minimum antenna area constraint. In this paper, we propose a HRWS imaging method based on multifrequency pulse diversity (MFPD) and azimuth multichannel (AMC) technique, capable of resolving range and Doppler ambiguities concurrently. In MFPD mode, range ambiguous echoes can be separated by matched filters in the range frequency domain, as multifrequency pulses are transmitted by a single channel in the transmitter. To further enhance the ambiguity resolution ability of the system and address azimuth incoherence, a novel scheme applying the MFPD to AMC system is proposed, categorized into two distinct scenarios. 1) Uniform Sampling: This scenario satisfies the displaced phase centre antenna condition. Under this condition, high range resolution imaging can be achieved through spectrum splicing in range frequency domain, simultaneously resolving Doppler ambiguity. 2) Non-uniform Sampling: In this scenario, the ambiguous echoes are processed by spatial digital beamforming and spectrum splicing in two-dimensional frequency domain. Finally, the HRWS imaging can be obtained by performing the traditional SAR algorithm. Furthermore, the proposed method can synthesize a wideband signal from multifrequency signals, thereby enhancing the feasibility of super-high-resolution imaging. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, China has launched a number of Gaofen (GF) series earth observation satellites. It is crucial to understand the relationship between the data of the GF series of satellite sensors for the selection of sensor images for scientific research. Taking the same-day transit image pairs of three regions as the study data, this paper compares the consistency of the Top of Atmosphere (TOA) reflectance of GF-1 WFV4 and GF-6 WFV sensors by using the TOA mean comparison method, and discusses the differences in water body and vegetation extraction. The results indicate that the satellite signal intensity in different land cover types and areas differs significantly. In the bare soil-dominated region, GF-1 WFV4 has a larger signal strength than GF-6 WFV, while in the vegetation-dominated region, it turns out to be just the opposite. The difference between the two sensors is mainly related to the difference in the spectral response function and the radiometric resolution of the two satellites. In addition, the determination coefficient (R2) of the corresponding bands of the two sensors is all greater than 0.90, indicating that the two sensors have strong linear correlation and good complementarity. [ABSTRACT FROM AUTHOR]

Historical fine-scale information of archaeological landscapes is crucial in archaeological investigations. However, documenting such information using satellite sensor data prior to 2000 remains a daunting challenge. Images from the declassified archives of KH-9 HEXAGON (KH-9) cameras, including the panoramic camera system (PCS) and mapping camera system (MCS), offer fine-scale information about archaeological sites. However, noise, contrast distortion and the availability of only a single panchromatic band can limit their potential, particularly for identifying features in subtropical climates within heterogeneous landscape types. This paper focuses on developing a novel multifaceted analytical framework with two components: image pre-processing and feature identification. The image pre-processing component is divided into two steps. First, a trained stationary wavelet transform (SWT) based on the normalized sill (NS) is developed to not only de-noises the image, but also preserve its original image characteristics. Then, the contrast of the de-noised images is optimized by the multi-resolution Top-hat (MTH) using multi-scale information. In the feature identification component, the MCS image is analysed using spatial colour composite write function memory (SCCWFM) and spatial novelty detection (SND). An ultra-fine spatial three-dimensional colour composite (UFSTCC) image and ultra-fine spatial digital surface model (UFSDSM) are produced to aid interpretation of the KH-9 PCS images. The proposed processing pipelines were tested on KH-9 MCS and PCS images of the World Heritage site at Liangzhu Ancient City (LAC) in China, which is characterized by a subtropical climate and a heterogeneous landscape types. The proposed pre-processing pipeline improved considerably the appearance of these images across the LAC landscape while maintaining the original image information. The developed digital analytical approaches for KH-9 PCS and MCS images facilitated straightforward identification of archaeological features in the LAC. The proposed framework has the potential to increase exploitation of the available KH-9 images in archaeological applications. [ABSTRACT FROM AUTHOR]

The classification of very high-resolution satellite imagery remains a focal point in remote sensing, attracting increased attention across diverse scientific disciplines. Various classification methods, including pixel- and object-based techniques, have been proposed, and their performances and limitations have been discussed in the literature. This paper presents a hybrid method that combines the strengths of pixel- and object-based methods in image classification to minimize errors associated with the segmentation process, particularly under-segmentation errors in object-based image analysis. The core concept behind the method lies in categorizing segmented image objects as either homogeneous or heterogeneous based on their class probability. In this process, the estimated possibilities from the object-based classification model are considered, and segments are designated as homogeneous or heterogeneous using a user-defined threshold. The object-based classification model determines the class labels for homogeneous image objects, while the heterogeneous ones, containing pixels representing different land cover classes, are classified using the pixel-based model. The performance of hybrid classification models, created by varying thresholds, is analysed using high-resolution WorldView-3 and WorldView-2 imagery and compared with pixel- and object-based classification results. For the implementation of image classification methods, Canonical Correlation Forest (CCF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) were employed. The findings indicated that employing the suggested hybrid strategy with a threshold value selected within a specific range (e.g. between 60% and 80%) and employing a robust classification algorithm that provides class probabilities (e.g. CCF) results in a statistically significant improvement in overall accuracy compared to pixel and object-based methods, with gains of 5% and 4%, respectively. Visual analysis of the produced thematic maps revealed that the proposed method minimizes the salt-and-pepper effect associated with pixel-based classification while mitigating segmentation problems arising from object-based classification. [ABSTRACT FROM AUTHOR]

Long-term ground-based lidar observations are important in estimating the contribution of different sources (mostly volcanic eruptions and wildfires) to aerosol loading of the atmosphere and in developing and improving various climate models. In this paper, we carefully analyse aerosol scattering ratio profiles and aerosol layers, observed with 532-nm lidar measurements at the Siberian Lidar Station in Tomsk (56.48°N, 85.05°E) over the period 2018–2022, and identify the layers' potential sources. To compare aerosol loading over Tomsk for the last five years (2018–2022) with that for previous years, we also present and discuss time series of integrated aerosol backscatter coefficient (IABC) for three altitude ranges. The first range (15–30 km) reveals the pure stratospheric aerosol loading provided by volcanic eruptions, the second range (11–15 km) is responsible mainly for aftereffects of wildfire smoke plumes, and the third one (11–30 km) demonstrates aerosol contribution from both sources. In particular, we found for the period 2001–2022 that the annual average IABC reached its maximal values of 2.91 × 10−4 sr−1 (15–30 km) and 8.21 × 10−4 sr−1 (11–30 km) in 2022 due to the Hunga Tonga-Hunga Ha'apai volcanic eruption. The period 2020–2022 exhibited a significant increase in the relative aerosol content at altitudes of 11–15 km in the total aerosol loading at 11–30 km over Tomsk from 54.4% in 2020 to 64.8% in 2022, which indicates a shift of the main part of aerosol loading to altitudes of 11–15 km due to the increased wildfire activity in North America and north-eastern Asia. The Arctic polar vortex is also revealed to distort the real aerosol vertical distribution over Tomsk by replacing it with the aerosol distribution inside the vortex on measurement days when the vortex is over the lidar site. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) datasets are used widely for estimating forest biometrics. The transferability of predictive models among ALS acquisitions is a topic of research due to differences in timing, flight parameters, equipment specifications, environmental conditions, and processing methods. The transferability of predictive models therefore is subject to uncertainty. This paper presents an evaluation of the transferability of models for the estimation of stem volume and diameter at breast height (DBH) based on individual tree crown size and competitive neighbourhood metrics derived for managed loblolly pine (Pinus taeda) and slash pine (Pinus elliottii) forest in the Southern USA. Two predictive models types were tested: multiple linear regression (MLR) and Rand Forest (RF). We also evaluated the inclusion of additional training data to model development. Models were able to be transferred to other locations with similar structural and management conditions as the original training dataset with little decrease in accuracy, specifically unthinned stands, despite different ALS acquisitions (Plot stem volume: R2 0.7–0.8; NRMSE 10–12%; mean DBH: R2 0.4–0.7; NRMSE 10–17%; plot basal area: R2 0.7–0.8; NRMSE 12%). Increases in structural differences between the training and test data, driven by age or thinning status, introduced unacceptable levels of uncertainty (Stem volume: R2 0.4–0.7; NRMSE 12–16%; mean DBH: R2 0.4–0.5; NRMSE 18–20%; plot basal area: R2 0.5–0.6; NRMSE 22–40%). Generally, RF models most accuracy estimated DBH, and MLR for stem volume. Improvements to estimate accuracy can be achieved through the addition of relatively small datasets, representing features which were not present in the original data. ALS's ability to provide accurate and near-complete inventories of forests hold a great deal of potential for forest management. The existence of a transferable model that can be used across different acquisitions represents a saving in terms of cost and time, we would argue that future research is therefore warranted. Novel LiDAR-based allometric models were tested using new LiDAR acquisitions; LiDAR predictions were based on tree crown size and immediate neighbourhood metrics; Model inputs were derived from drone and crewed aircraft LiDAR acquisitions; Model predictions were most accurate when applied to similar stand structures as the original model; Normalized root means square error for stem volume and diameter in pine stands with no thinning was <15%; Stem size estimates in thinned stands were consistently overestimated. [ABSTRACT FROM AUTHOR]

The polarization information of incident light gets lost after passing through the scattering medium such as haze. This phenomenon, called depolarization, poses a challenge to polarization remote sensing of ships in sea-haze weather. In this paper, we propose the concept of ship polarization information restoration for the first time and propose a ship polarization information restoration method for the benefit of ship remote sensing in sea haze weather. The approach is based on the estimation of airlight, a component of hazy polarimetric images. To demonstrate the effectiveness of the proposed ship polarization information restoration method, we conduct an outdoor experiment on ship polarization information restoration in sea haze weather. Experimental results show that sea haze significantly degrades the degree of polarization of ships, but slightly affects the angle of polarization. In addition, the degree of polarization of ships becomes more prominent after polarization information restoration. [ABSTRACT FROM AUTHOR]

Parts of sections in permafrost regions of the Qinghai-Tibet Railway (QTR) have been deformed due to permafrost degradation, the global warming and human activities. Therefore, monitoring the subgrade settlement of the QTR in permafrost regions has been a major working in QTR maintenance. For this reason, this paper investigates the subsidence characteristics and derivative mechanisms of railway subgrades in the permafrost regions of the QTR. Additionally, it analyses the impact mechanisms of surface temperature changes on the subsidence of permafrost subgrades. Building on this investigation, a C-I-GWO-BP neural network prediction model was developed to forecast subgrade settlement in permafrost regions. The findings reveal a substantial correlation (R2 = 0.834) between the deformation of railway subgrades in permafrost regions and changes in surface temperature. Meanwhile，with the intensification of global warming and human activities, the subsidence of permafrost subgrade has shown an increasing trend year by year. The primary areas of deformation are predominantly concentrated in ice-rich permafrost regions, including rivers, lakes, and snowmelt runoff areas. Additionally, when compared to traditional BP neural network models, the C-I-GWO-BP neural network prediction model proposed in this article showcases diminished prediction errors and heightened accuracy in identifying optimal weights and thresholds. This model offers technical support for the efficient, accurate, and automated prediction of railway subgrade subsidence in permafrost regions. [ABSTRACT FROM AUTHOR]

The recent deployment of new satellite-based hyperspectral imaging sensors, along with advances in classification methods, has enhanced the acquisition of information on the spatiotemporal patterns of Land Use/Land Cover (LULC).. With this context, the study presented in this paper seeks to assess the combined utilization of the Pixxel's technology demonstrator hyperspectral satellite mission with the Support Vector Machines (SVM) and Random Forest (RF) machine learning classifiers for LULC mapping in an agricultural area located in Queensland, Australia. Accuracy assessment of the resulting LULC maps has been done based on relevant classification accuracy metrics. It has been observed that SVMs outperformed RF in terms of both overall classification and urban vegetation cover mapping accuracy. An overall accuracy of 92.34% with kappa of 0.90 was reported for the SVMs' results, whereas for RF, the accuracy was 89.65% with 0.89 kappa, respectively. Both classifiers produced similar results for the classes of mango tree and sugarcane crops, whereas minor differences were observed in the depiction of water bodies and artificial surfaces. Our study findings provide insightful perspectives on the potential of Pixxel datasets for delineating the spatiotemporal distribution of LULC in agricultural landscapes, showcasing Pixxel's hyperspectral sensors' promising potential in this field. [ABSTRACT FROM AUTHOR]

Distributed Inverse Synthetic Aperture Radar (ISAR) systems employ multiple spatially separated radars to observe a target, with the potential to enhance radar imaging azimuth resolution and gather more target information. Due to the existence of gaps in the observation angles of each radars, the coherence between pulses of radar echo is disrupted during fusion imaging, making it challenging for the Range-Doppler (RD) algorithm to achieve well-focused images. This paper proposes an imaging method based on sparse signal recovery (SSR), using the reweighted ${l_1}$ l 1 norm with convolution and total variation as regularization terms. This approach not only yields well-focused ISAR images but also better preserves the overall target information compared to other methods, while also exhibiting excellent noise resistance. In addition, the alternating direction method of multipliers (ADMM) algorithm is utilized for the solution to reduce computational complexity. Simulation and measured data verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Acquiring precise change map for synthetic aperture radar (SAR) images poses challenges due to the absence of labelled datasets and the presence of speckle noise interference. This paper presents a two-stage fusion strategy coupled with capsule network classification detection method. Initially, to enhance the structure of change areas within the difference image (DI), we propose an energy ratio (ER) algorithm based on the mean ratio (MR). Subsequently, employing a two-stage fusion strategy integrating multiplication and discrete wavelet transform (DWT), we fuse the DIs generated by the three algorithms to effectively complement change information across distinct DIs. In addition, a saliency detection algorithm is introduced to mitigate noise interference and balance the number of samples. Hierarchical fuzzy c-means clustering (HFCM) is then used to pre-classify the salient fusion difference image (SFDI) and derive pseudo-labels. Finally, we apply the residual shrinkage capsule network (RSCN) to reclassify pre-classification results and obtain the change detection outcome, capable of extracting the multi-scale refinement features and adaptively learning discriminant information from changed and unchanged regions. Experimental evaluations on various SAR image datasets demonstrate the exceptional detection performance of the proposed method. [ABSTRACT FROM AUTHOR]

Domain adaptation (DA) offers an effective way to align feature distributions of the source domain (SD) and the target domain (TD) without requiring any target label samples. As a method of DA, representation learning effectively realizes the alignment of feature distributions in different domains by transferring domain knowledge. However, existing representation learning methods often focus on unilateral representation transfer, which potentially results in transfer bias. Additionally, most methods ignore the connection between domain alignment and discrimination during the DA process, which easily causes negative transfer. This paper proposes a dynamic weighted dual-driven domain adaptation (DW-D $^3$ 3 A) model that effectively addresses the aforementioned issues through bilateral feature transfer between domains and a dynamic weighted scheme. Technically, we first propose a dual-driven domain adaptation (D $^3$ 3 A) model, which employs symmetrical structures to facilitate the knowledge transfer of bilateral representations between source and target domain samples, learning the subspaces of two domains and reducing distribution discrepancies between subspaces via joint distribution-driven alignment. This process mitigates transfer bias and goes beyond previous unilateral transfer methods. Then, to alleviate strong constraints on projecting SD and TD into the same subspace in existing approaches, we apply a relaxed subspace constraint to bring the projections of SD and TD closer. Furthermore, data reconstruction is incorporated to preserve discriminant information from the original data. Lastly, we expand (D $^3$ 3 A) to DW-D $^3$ 3 A using a dynamic weighted scheme, which adjusts the weights assigned to domain alignment and discrimination based on their significance to inhibit negative transfer. Extensive experimentation on three datasets indicates that DW-D $^3$ 3 A outperforms seven other DA methods, showing its superior performance. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) has been a vital technique in hyperspectral image processing. Over the past years, many algorithms have been developed based on the nonnegative matrix factorization (NMF) framework. However, the conventional methods based on the NMF need prior information of the number of endmembers, which is usually estimated by a separate procedure. Therefore, the precision of estimating the number of endmembers will impact the subsequent unmixing effect. Thus, collaborating between the two processes is desired to avoid the propagation of errors. This paper proposes a new NMF-based method, which can estimate the number of endmembers and extract the endmembers and the abundance maps. Firstly, we introduce the weighted group sparsity regularization to adaptively eliminate the abundances corresponding to the redundant endmembers, while the abundances of the true endmembers are preserved. Then the number of endmembers can be determined by the reconstruction error function and the ${l_2}$ l 2 -norm of the abundance vectors. Secondly, since the simplex determined by the endmembers has the minimum volume among all simplexes that circumscribe the data points, we impose the total variational regularization to force the endmembers to be close to each other to minimize the simplex volume. And because the total variational regularization avoids the use of noisy pixels, it can decrease the influence of noise and improve the robustness of the solution. For the proposed model, we design an effective algorithm to solve it. Also, the experimental results based on the synthetic data and real data demonstrate that the proposed method can identify the endmembers number well and achieve robust unmixing results. [ABSTRACT FROM AUTHOR]

In recent years, with the development of deep learning, SAR image-based ship target-detection technology has become a current research hotspot. SAR images are characterized by complex backgrounds, significant speckle noise interference, and poor interpretability. To address this challenge, this study proposes a new Gaussian circle radius determination strategy and a multi-level feature fusion codec-based ship detection network (MFFC-SDN) for SAR images. Differing from CenterNet's corner-oriented Gaussian circle strategy, the Gaussian circle strategy proposed in this paper takes the centre point coordinates of the real target as the origin to define a circular region. This approach allows for more precise supervision of the training model. Meanwhile, we propose MFFC-SDN to solve the problem of the poor performance of CenterNet in SAR ship detection. MFFC-SDN is an anchor-free, single-stage target-detection method with a low computation cost. In MFFC-SDN, we introduce a Multilevel Feature Recapture Codec Module (MFR-CM) to enhance the feature extraction capability of the network by using the features of the encoding stage twice because of the easy loss of image features in the lengthy codec process of the CenterNet network backbone. A Residual Attention Pyramid Feature Fusion Module (RA-PFFM) is designed to enhance the feature map and obtain multi-scale features. Experimental results on the SSDD and SAR-ship-dataset show that MFFC-SDN significantly enhances SAR image target-detection performance, effectively addressing detection challenges related to large target size variations and complex environmental conditions. Compared with existing algorithms, MFFC-SDN achieves the highest $A{P^{50}}$ A P 50 . [ABSTRACT FROM AUTHOR]

The current building surface reconstruction from airborne LiDAR point clouds pays more attention to the relationships between roof primitives but ignores the possible existing points of walls and the structural relationships between the roofs and walls. In this paper, we propose a novel approach that reconstructs a building surface model from airborne LiDAR point clouds based on improved structural constraints. Unlike existing hypothesis-and-selected-based methods, which impose the same constraints on all relationships between planar primitives, we design a series of strong and soft structural constraints to promote the representation of geometric and structural details of both roofs and walls in the reconstructed models. To be precise, we generate a set of candidate faces with detected planar primitives of building and vertical planes inferred from the corresponding footprint to complete possibly missing walls. Then, the potential structural representations associated with building walls in the original point clouds are identified by conflict detection between the support planes both existing points of building walls and inferred from the footprint, and on this basis, improved structural constraints are constructed for the structures consisting of different type of support planes in the set of candidate faces. Finally, a compact and watertight model is extracted from the faces of the candidate set by energy minimization with proposed improved structural constraints. Experiments on the various airborne LiDAR point clouds and aerial point clouds datasets have demonstrated that the proposed method could obtain accurate and compact models with the representation of geometric and structural details between building roofs and walls compared with other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The precipitation estimation with high spatial and temporal resolution over Tibet is of great significance for monitoring the stability of the plateau ecosystem and early warning of natural disasters. In response to the problem of the unsatisfactory accuracy in traditional real-time precipitation inversion methods and quantitative accuracy estimation products in this region. This paper adopts deep-learning methods to explore the corresponding relationship between precipitation and multi-band observation data from geostationary satellite imagery. The U-High Resolution Network (U-HRNet) as backbone is fully exploited to generate multi-scale features and fused using attention gates to extract high-resolution spatial representations representing precipitation clouds, thereby achieving precipitation estimation of target clouds at different scales. In addition, in response to the characteristics of high precipitation intensity and drastic cloud cluster changes in summer convective weather within a short period of time, a multi-head attention mechanism is introduced to extract temporal information from time series inputs and fuse it with spatial features to improve the accuracy of real-time precipitation estimation. Compared with the operational precipitation products and baseline deep-learning models, the classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR) and Critical success index (CSI) show that the proposed model (U-HRNet+) has better performance in test set and has high generalization ability for estimating short-term heavy rainfall over Tibet and its surrounding areas in summer. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. [ABSTRACT FROM AUTHOR]

Seagrass beds play a vital ecological role by regulating climate, maintaining biodiversity, and sequestering carbon. Achieving accurate classification of seagrass beds across both time and space is of significant importance for efficient monitoring. This paper addresses the challenges of seagrass bed classification in remote sensing imagery and the difficulty of achieving temporal and spatial transferability of classification model. Leveraging a long-term Landsat satellite image time series spanning from 1976 to 2022 in the Yellow River Delta, we enhance the DeepLab V3+ network and propose a cross-temporal-spatial domain seagrass bed transfer learning classification approach. We utilize selectively chosen images from a few years in partial regions for training, introduce freeze-training and-unfreeze-training during the model training phase, and then transfer the model to achieve cross-temporal and cross-temporal-spatial domain classification on images from other years and regions. Additionally, we discuss the factors influencing the classification results. The research results indicate that: (1)The method we proposed has shown good results, achieving classification accuracy of 83.17% and 80.99% for seagrass beds in both cross-temporal and cross-temporal-spatial domains. (2) The classification results indicate that this method achieves an average classification accuracy higher by 16.18% compared to networks such as UNet, PSPNet, HRNet, UNet++, SegFormer, etc. under cross-temporal conditions and higher by 17.56% under cross-spatiotemporal conditions for seagrass bed classification. (3) Based on the classification results, it is observed that from 1973 to 1986, the overall area of seagrass beds in the Yellow River Delta had an initial growth trend, with the largest seagrass bed area recorded in 1986, reaching 4281.289 hectares. Subsequently, the seagrass beds showed an overall decreasing trend, and by 2019 they had almost completely disappeared. The cross-temporal-spatial domain seagrass bed transfer learning classification method proposed in this paper can provide valuable support for the monitoring, restoration, management, and utilization of seagrass ecosystems. [ABSTRACT FROM AUTHOR]

Classification of hyperspectral image (HSI) is an essential step in various remote sensing tasks. Currently, transformer-based models have made significant contributions to HSI classification due to their remarkable ability to model the relation among long-range sequence data. However, HSI data exhibit strong nonlinear coupling and strong correlation, leading to a decrease in the classification performance of transformer-based models. The SpectralFormer network has achieved excellent performance in HSI classification, but its cross-layer adaptive fusion mechanism cannot effectively highlight critical information, affecting the representation ability of spectral-spatial features. To address the above issues, this paper proposes an HSI classification method based on local feature decoupling and SpectralFormer with a hybrid attention module (HAMSF) network. Specifically, this paper first uses the histogram of oriented gradient algorithm to preprocess HSI coupled data and achieve preliminary nonlinear decoupling of HSI data. Secondly, the hybrid attention module is embedded in a cross-layer adaptive fusion mechanism, generating refined neighbouring information before each information fusion to improve the spectral-spatial feature representation ability. Finally, the processed data are input into the HAMSF network for the final decoupling and classification of HSI. The experiment is carried out on three benchmark hyperspectral datasets, and the experimental results confirm that the proposed method has better classification performance compared to several transformer-based and classical HSI classification methods. [ABSTRACT FROM AUTHOR]

Initialization plays an important role in the accuracy of endmember extraction algorithms (EEAs) in linear hyperspectral unmixing (LHU). Random initialization can lead to varying endmembers generated by EEAs. To address this challenge, an initialization strategy has been introduced, encompassing vertex component analysis (VCA), automatic target generation process (ATGP), among others. These techniques significantly contribute to enhancing the accuracy of EEAs. However, complex initialization is sometimes less preferable, prompting the unexplored question of whether there exists an EEA robust to initialization. This paper focuses on analyzing this issue within the context of minimum simplex volume-based (MV) methods, which have received considerable attention in the past two decades due to their robustness against the absence of pure pixels. MV methods typically formulate LHU as an optimization problem, most of which includes a non-convex volume term. Additionally, many MV methods use VCA as an initialization strategy. Firstly, this paper demonstrates that the variable splitting augmented Lagrangian approach (SISAL), as a representative non-convex MV method, heavily depends on initialization. To our knowledge, the impact of initialization for MV methods has not been thoroughly analyzed before. Furthermore, this paper proposes an initially robust MV method by introducing a new convex MV term. Numerical experiments conducted on simulated and real datasets demonstrate its outstanding performance in accuracy and robustness to initialization. Throughout the experiments the proposed method proves to be the most stable, which is crucial in real scene where the ground truth is unknown beforehand. [ABSTRACT FROM AUTHOR]

Classification with a few samples of training set has been a long-standing issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. In fact, one of the most important challenges of the PolSAR image classification task is the number of labelled samples. In essence, training network by utilizing just few numbers of training samples cannot be led to the reliable result since the neural network is so sensitive to the number of training samples. In addition, in the real PolSAR image classification task, the huge number of training samples is not accessible. On the other hand, classification performance using just small number of labelled samples is not an accurate result. So, in this paper, aiming at the small number of training samples of the PolSAR image classification task, a novel ensemble self-supervised feature-learning (ESSFL) model is designed. The designed ESSFL can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, to utilize the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet-B0 is presented and utilized as the main section of the deep learning (DL) model to extract DL features of PolSAR data. Then, using an optimization function that constrains the cross-correlation matrix of various distortions of each sample to the identity matrix, the designed deep learning model can obtain the effective features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. Moreover, two ensemble learning (EL) models, feature-level and view-level ensemble, are proposed to increase the feature extraction capability and classification result by jointly using spatial features at different scales and polarimetric information at different bands. Finally, the stack of the obtained features and the main polarimetric information of PolSAR data can be input into classifier for the classification of PolSAR data. In this paper we use two different classifiers, the first one is the Random Forest (RF) classifier and the second one is the Support Vector Machine (SVM) classifier. The advantages of SVM include that it can be used to avoid the difficulties of using linear functions in the high-dimensional feature space, and the optimization problem is transformed into dual convex quadratic programs that are so usable for an actual image classification task. In addition, among all the available classification methods, random forests provide one of the highest accuracies. The random forest technique can also handle big data with numerous variables running into thousands. In fact, it can automatically balance data sets when a class is more infrequent than other classes in the data that is so important for a real PolSAR image classification task. Experimental results on three well-known PolSAR data sets illustrate that the designed ESSFL can extract more discriminant features using the designed deep learning model. In the end, the experiments prove that the designed ESSFL has a significant classification performance compared with different deep learning models in the case of small number of training samples, and also it can achieve a better result in the case of large number of training samples by comparison with the most of the deep learning models. [ABSTRACT FROM AUTHOR]

The 2-D compact variational mode decomposition (2-D-C-VMD) has potential research significance in data mining of hyperspectral images (HSIs). The paper proposes a method of concatenation of features. First, the 2-D-C-VMD technique is used to decompose the HSIs to obtain smooth and clear boundary sub-band images when the 2-D-C-VMD takes the most preferred a priori parameter K ×. Second, the optimal low-frequency component features of the image and the optimal high-frequency component constructed features are concatenated first and last, and the structure of the concatenation is constructed into a new fused data. Finally, in order to avoid the redundancy of the fused data band information from affecting the classification result, combine a recently developed simple and practical superpixelwise principal component analysis (SuperPCA) dimensionality reduction method to effectively reduce the dimensionality of the fused data. By comparing the analysis with relying only on the optimal low-frequency image feature information and with the direct linear superposition fusion that has been employed, it is verified that the concatenation of features approach proposed in this paper can obtain better recognition results. [ABSTRACT FROM AUTHOR]

Multilabel remote sensing image classification (MLRSIC) has received increasing research interest. Taking the co-occurrence relationship of multiple labels as additional information helps to improve the overall performance. However, current methods only focus on using it to constrain the final feature which is output from a convolutional neural network (CNN). On the one hand, these methods need to exploit the potential of label correlation in feature representation fully. On the other hand, they increase the label noise sensitivity of the system, resulting in poor robustness. In this paper, a novel method called 'Semantic Interleaving Global chaNnel Attention' (SIGNA) is proposed for MLRSIC. First, the label co-occurrence graph is obtained according to the statistical information of the training set and fed into a graph neural network (GNN) to generate optimal semantic feature representations of each label. Next, the semantic features are interleaved with visual features which are extracted by CNNs to guide the overall features of the input image transform from the original feature space to the semantic feature space with embedded label relations. Then, global attention triggered by semantic interleaving is used to emphasize visual features in important channels. Finally, to make SIGNA easier to use and more optimized, multihead SIGNA-based feature adaptive weighting networks are proposed as plug-in blocks to plug into any layers of a CNN. For remote sensing images, better classification performance can be achieved by inserting the plug-in blocks into the shallow layers of CNNs. We conducted extensive experimental comparisons on three data sets: UCM, AID and DFC15. Experimental results demonstrate that the proposed SIGNA achieves superior classification performance compared to state-of-the-art (SOTA) methods. Notes that the codes of this paper will be open to the community for reproducibility research. [ABSTRACT FROM AUTHOR]

Building roofs are crucial elements that must be extracted from satellite images for use in services like updating geodatabases, risk analysis and rescue maps. The diversity and complexity of the building structures present challenges for accurate and efficient building rooftop extraction from very high resolution (VHR) images. The significance of building extraction from satellite imagery in various applications has attracted the attention of the research community in this domain. In this paper, the progression of the research in building extraction is presented based on two streams, such as Traditional building extraction techniques and Evolving DL-based building extraction techniques. Furthermore, the categorization of traditional techniques into transform-based techniques, indices-based methodologies and machine learning-based methodologies are summarized. An enhanced comprehension of the challenges associated with extracting buildings from aerial images using DL models is given. A performance analysis of the popular Deep Learning models used for building extraction, such as UNet, SegNet, ResUNet, UNet++, ENRUNet and Dilated ResUNet, in terms of Accuracy, IoU and Dice Loss is presented. We hope that this paper will improve researchers' understanding of the problems associated with extraction of buildings from images and ultimately to develop new models with better performance. [ABSTRACT FROM AUTHOR]

The fusion of hyperspectral and multispectral images presents a challenge as it involves blending a low-resolution hyperspectral image (HSI) with a corresponding multispectral image (MSI) to produce a high-resolution hyperspectral image (HRI). A number of existing techniques have limitations; for instance, matrix decomposition-based approaches fail to retain adequate spatial and spectral image information during fusion, while tensor decomposition-based processes have high computational overhead. In this paper, we propose a novel method for fusing hyperspectral and multispectral images. Our method leverages the strong correlation among the spectral bands of hyperspectral images and employs the SVD technique to extract spectral feature subspaces. This approach results in a more compact and representative feature space for fusion. Secondly, the proposed method utilizes Elastic Net regularization in combination with ${L_1}$ L 1 and ${L_2}$ L 2 regularization for effective feature selection of highly covariate features. Weighted group sparse regularization is employed to enhance the fusion effect, enabling better representation of the image's structure and features. The algorithm is subsequently evaluated on multiple datasets to confirm its effectiveness. The results of the experiment suggest that the suggested algorithm greatly enhances the spatial resolution and visual clarity of HSI images while preserving the spectral characteristics when compared to conventional methods for fusion of hyperspectral images. Additionally, the constraint for regulation can competently reduce any noise or artefacts, thereby boosting image discrimination. To summarize, the utilization of a sparse tensor-based hyperspectral image fusion algorithm with subspace learning provides an efficacious approach for processing hyperspectral imagery. This method is capable of improving spatial resolution, extracting advantageous features from hyperspectral imagery, and ultimately supports the process of remote sensing image analysis and application. [ABSTRACT FROM AUTHOR]

Spectral clustering, as an algorithm based on graph theory and spectral theory, has shown excellent performance in the classification tasks of hyperspectral images in recent years. Although better results have been achieved, some challenges still exist. The inclusion of a priori information can increase the performance of spectral clustering algorithms; however, in practice, it is often unable to meet the demand for a priori information; at the same time, spectral clustering for the quality of the similarity matrix is demanding, and some of the current algorithms are to improve the similarity matrix from the aspect of data planning, and ultimately for the label feature matrices are diluted. In response to the above problems, this paper proposes a self-supervised spectral clustering with spectral embedding (SESSC). The algorithm obtains a low-dimensional representation of the data through spectral embedding, which can simplify clustering while preserving feature information; then uses the similarity matrix about the data and the guidance of the sample constraint information to obtain a new similarity matrix in order to further refine the structural graph, and the result can feed back and optimize the label feature matrix and the low-dimensional representation of the data. Additionally, we introduce a fractional theory in the update of the sample variable matrix, which assures the integrity and validity of the information in the update. Experimental results show that the proposed algorithm has better performance in hyperspectral image classification than existing spectral clustering algorithms. [ABSTRACT FROM AUTHOR]

There are challenges for cloud detection over bright surfaces, especially for Sentinel-2 image without thermal band. In this paper, we developed a new cloud detection algorithm for Sentinel-2 data designed to tackle the challenges of cloud detection on bright targets. Our algorithm (called RF-NDTeI-SNIC) was built through the integration of Random Forest (RF) model, Normalized Difference Temporal Index (NDTeI) cloud index and Simple Non-Iterative Clustering (SNIC) image segmentation algorithm. First, RF and NDTeI were used to derive the initial and second stage's cloud detection results, respectively. Then, the RF- and NDTeI-based results were fused to remove the bright surfaces and land cover changes. Finally, the fusion results were further refined using SNIC for morphological processing. The proposed RF-NDTeI-SNIC algorithm was evaluated using nine challenging cloud-covered Sentinel-2 scenes with substantial bright surfaces. Results indicated that the average overall accuracy of our algorithm was 95.01%, with average commission rate of 10.19% for cloud. In addition, five commonly used Sentinel-2 cloud mask algorithms including s2cloudless, QA, Fmask, CDI and Tmask were selected for comparative analysis. Results suggested that our algorithm outperformed others with overall accuracies of 89.60–94.17% and cloud commission rate of 9.46–37.50%, and have significant advantages in terms of bright surfaces. In summary, the RF-NDTeI-SNIC algorithm we developed was capable of yielding accurate Sentinel-2 cloud masks, and the novel NDTeI cloud index we proposed gave a new and effective approach for improving the separability of clouds and bright surfaces. [ABSTRACT FROM AUTHOR]

Radar signatures of rain over the ocean have a complex structure since they receive contributions from surface scattering and volume scattering and attenuation by hydrometeors in the atmosphere. These contributions overlap and are often difficult to detangle. While most of the mechanisms contributing to radar signatures of rain over the ocean are well understood, there is one remaining issue that has been discussed controversially in the literature for a long time. It is the question what scattering mechanism causes the areas of strongly enhanced radar backscatter, also called 'bright blobs' or 'bright patches', which are frequently observed on spaceborne C-band SAR images acquired over tropical and subtropical oceans in the presence of convective rain. Recently, papers have been published in which it is hypothesized that they are caused by radar backscattering at hydrometeors in the melting layer (ML). Although many observational facts seem to support this hypothesis, there exists one strong argument against this hypothesis: It is the observation that the position of the ML radar signatures (bright blob) in the SAR image is not shifted in anti-range from the position, where the rain column hits the sea surface. This absence of a shift is observed when 1) comparing Sentinel-1 SAR images on which rain cells are visible with quasi-concurrently acquired weather radar images and 2) when inter-comparing of SAR images of rain cells acquired concurrently at different frequencies and polarizations. Based on these observations, we discard the hypothesis that the bright blobs are due to volume scattering at hydrometeors in the ML and hypothesize instead that they are due to scattering at splash products at the sea surface. This hypothesis is supported by radar backscattering measurements carried out in the laboratory and from a shore-based platform, which show that, at C-and X-band, strong rain can give rise to strong radar returns also at cross-polarization. [ABSTRACT FROM AUTHOR]

Hyperspectral images are easily contaminated by multi-modal noise in the process of data acquisition. Generally, hyperspectral signatures and noise appear unexpectedly scattered in the spatial-spectral domain. Conversely, in the transform domain, the principle components of an image are gathered in the low-frequency bands, while the details and noise often occur in high-frequency parts. The existing denoising methods usually divide the whole frequency domain into two parts, that is, high frequency and low frequency, which can not make the most of the knowledge contained at different frequencies or scales, especially in the multiscale transform domain. Furthermore, aiming to restore a clean image from the noise-corrupted data, hyperspectral image denoising often amounts to an ill-posed problem, in which reshaping cubic data into matrix form results in the loss of structure information. To address the aforementioned issues, this paper incorporates the reweighted smoothing regularization in the transform domain into the noise-perturbed degradation model to reformulate a novel optimization problem. First, the multiscale curvelet is leveraged to transform the spatial matrix into frequency domain, where the coefficients are carefully designed to weight the components at different scales and orientations. Second, ${l_1}$ l 1 -norm is imposed on the abundance term to promote sparsity in the transform domain for performance enhancement. Finally, using proximal alternating optimization, an efficient algorithm is well developed to obtain the closed-form solutions to the proposed denoising problem. The experimental results on several synthetic and real datasets, illustrate that the proposed method improves PSNR by at least 1.5 dB and reduces ERGAS error by more than 35% than the state-of-the-art approaches under the multi-modal noise mixed environments, which verifies the validity of regularization terms in hyperspectral images denoising. [ABSTRACT FROM AUTHOR]

In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m. [ABSTRACT FROM AUTHOR]

Marine mucilage, also known as sea-snot, is an environmental disaster with ecological, economical, and public health-related impacts, which is occurring with increasing frequency and severity worldwide. One of the latest mucilage occurrences, the mucilage outbreak of Spring 2021 in the Sea of Marmara, lasted for well over three months and covered more than area of 1160 $k{m^2}$ k m 2 , and was repeated in 2022, although to a smaller extent in terms of duration, coverage area, and severity. Remotely sensed images acquired by Earth observation sensors provide a convenient and promising alternative for environmental monitoring of marine mucilage, with respect to field and laboratory work which are costly, require expert knowledge, and which have to be conducted in a small time-window and have limited generalizability due to the under-sampling of a large medium with a highly dynamic character. This work presents an unmixing-based approach using nonnegative matrix factorization for marine mucilage monitoring from remotely sensed hyperspectral data. The proposed method enables to identify the spectral signature of mucilage, and its variants where applicable, and the precise detection and estimation of mucilage concentrations in affected regions. Additionally, the proposed approach facilitates monitoring the temporal variations of mucilage presence, distribution, and accumulation, between data acquired from the same region at different dates or times. This enables quantitative spatio-temporal mapping of distributions and the potential of assessing the severity of outbreaks, without the requirement of manually prepared ground truth maps or carefully selected thresholds. Real hyperspectral data acquired by the PRISMA mission are used to validate the proposed approach qualitatively and quantitatively. The paper also discusses future directions and recommendations to enhance the performance and applicability of the proposed approach for marine mucilage analysis, and environmental monitoring in general. [ABSTRACT FROM AUTHOR]

The intelligent segmentation of high-resolution remote sensing (HRS) image, also called as dense prediction task for HRS image, has been and will continue to be important research in the remote sensing community. In recent years, the growing wave of artificial intelligence (AI) technology has introduced innovative paradigms to this domain, yielding outstanding results and overcoming many challenges with conventional segmentation techniques. This paper provides a comprehensive review of these intelligent segmentation methodologies, including traditional pattern recognition, convolution neural network (CNN)-based, and Transformer-based techniques. However, the explosive but incomplete development of intelligent segmentation techniques also poses more challenges for earth observation experts, the most of which is the technical interpretability. Consequently, we consider these segmentation techniques in the aspect of explainable artificial intelligence (XAI). Data-centric XAI thinks the practical applications of the segmentation model while model-centric XAI will facilitate the understanding of decision-making processes and the adjustment of structural features. Moreover, this review identifies novel research questions and provides constructive insights and recommendations to HRS image segmentation tasks, which may shed new light on the intelligent segmentation methods within the remote sensing image understanding community. [ABSTRACT FROM AUTHOR]