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Monitoring aircraft using synthetic aperture radar (SAR) images is a very important task. Given its coherent imaging characteristics, there is a large amount of speckle interference in the image. This phenomenon leads to the scattering information of aircraft targets being masked in SAR images, which is easily confused with background scattering points. Therefore, automatic detection of aircraft targets in SAR images remains a challenging task. For this task, this paper proposes a framework for speckle reduction preprocessing of SAR images, followed by the use of an improved deep learning method to detect aircraft in SAR images. Firstly, to improve the problem of introducing artifacts or excessive smoothing in speckle reduction using total variation (TV) methods, this paper proposes a new nonconvex total variation (NTV) method. This method aims to ensure the effectiveness of speckle reduction while preserving the original scattering information as much as possible. Next, we present a framework for aircraft detection based on You Only Look Once v8 (YOLOv8) for SAR images. Therefore, the complete framework is called SAR-NTV-YOLOv8. Meanwhile, a high-resolution small target feature head is proposed to mitigate the impact of scale changes and loss of depth feature details on detection accuracy. Then, an efficient multi-scale attention module was proposed, aimed at effectively establishing short-term and long-term dependencies between feature grouping and multi-scale structures. In addition, the progressive feature pyramid network was chosen to avoid information loss or degradation in multi-level transmission during the bottom-up feature extraction process in Backbone. Sufficient comparative experiments, speckle reduction experiments, and ablation experiments are conducted on the SAR-Aircraft-1.0 and SADD datasets. The results have demonstrated the effectiveness of SAR-NTV-YOLOv8, which has the most advanced performance compared to other mainstream algorithms. [ABSTRACT FROM AUTHOR]

Rapid advancements in satellite technology have led to a significant increase in high-resolution remote sensing (RS) images, necessitating the use of advanced processing methods. Additionally, patent analysis revealed a substantial increase in deep learning and machine learning applications in remote sensing, highlighting the growing importance of these technologies. Therefore, this paper introduces the Kolmogorov-Arnold Network (KAN) model to remote sensing to enhance efficiency and performance in RS applications. We conducted several experiments to validate KAN's applicability, starting with the EuroSAT dataset, where we combined the KAN layer with multiple pre-trained CNN models. Optimal performance was achieved using ConvNeXt, leading to the development of the KonvNeXt model. KonvNeXt was evaluated on the Optimal-31, AID, and Merced datasets for validation and achieved accuracies of 90.59%, 94.1%, and 98.1%, respectively. The model also showed fast processing speed, with the Optimal-31 and Merced datasets completed in 107.63 s each, while the bigger and more complicated AID dataset took 545.91 s. This result is meaningful since it achieved faster speeds and comparable accuracy compared to the existing study, which utilized VIT and proved KonvNeXt's applicability for remote sensing classification tasks. Furthermore, we investigated the model's interpretability by utilizing Occlusion Sensitivity, and by displaying the influential regions, we validated its potential use in a variety of domains, including medical imaging and weather forecasting. This paper is meaningful in that it is the first to use KAN in remote sensing classification, proving its adaptability and efficiency. [ABSTRACT FROM AUTHOR]

Advances in deep learning and computer vision techniques have made impacts in the field of remote sensing, enabling efficient data analysis for applications such as land cover classification and change detection. Convolutional neural networks (CNNs) and transformer architectures have been utilized in visual perception algorithms due to their effectiveness in analyzing local features and global context. In this paper, we propose a hybrid transformer architecture that consists of a CNN-based encoder and transformer-based decoder. We propose a feature adjustment module that refines the multiscale feature maps extracted from an EfficientNet backbone network. The adjusted feature maps are integrated into the transformer-based decoder to perform the semantic segmentation of the remote sensing images. This paper refers to the proposed encoder–decoder architecture as a semantic feature adjustment network (SFA-Net). To demonstrate the effectiveness of the SFA-Net, experiments were thoroughly conducted with four public benchmark datasets, including the UAVid, ISPRS Potsdam, ISPRS Vaihingen, and LoveDA datasets. The proposed model achieved state-of-the-art accuracy on the UAVid, ISPRS Vaihingen, and LoveDA datasets for the segmentation of the remote sensing images. On the ISPRS Potsdam dataset, our method achieved comparable accuracy to the latest model while reducing the number of trainable parameters from 113.8 M to 10.7 M. [ABSTRACT FROM AUTHOR]

Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines were created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak-seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data are extracted by the statistics of the wave number locations with high frequency. The attention mechanism was used for the peak data, and the attention mechanism was weighted to the feature map of the feature extraction block. The training set, validation set and prediction set were divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, the CWT-AM-CNN proposed in this paper was compared with the classical classifier algorithm based on CO2 feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets was as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets. [ABSTRACT FROM AUTHOR]

Small UAV target detection and tracking based on cross-modality image fusion have gained widespread attention. Due to the limited feature information available from small UAVs in images, where they occupy a minimal number of pixels, the precision required for detection and tracking algorithms is particularly high in complex backgrounds. Image fusion techniques can enrich the detailed information for small UAVs, showing significant advantages under extreme lighting conditions. Image registration is a fundamental step preceding image fusion. It is essential to achieve accurate image alignment before proceeding with image fusion to prevent severe ghosting and artifacts. This paper specifically focused on the alignment of small UAV targets within infrared and visible light imagery. To address this issue, this paper proposed a cross-modality image registration network based on deep learning, which includes a structure preservation and style transformation network (SPSTN) and a multi-level cross-attention residual registration network (MCARN). Firstly, the SPSTN is employed for modality transformation, transferring the cross-modality task into a single-modality task to reduce the information discrepancy between modalities. Then, the MCARN is utilized for single-modality image registration, capable of deeply extracting and fusing features from pseudo infrared and visible images to achieve efficient registration. To validate the effectiveness of the proposed method, comprehensive experimental evaluations were conducted on the Anti-UAV dataset. The extensive evaluation results validate the superiority and universality of the cross-modality image registration framework proposed in this paper, which plays a crucial role in subsequent image fusion tasks for more effective target detection. [ABSTRACT FROM AUTHOR]

Cables are vital load-bearing components of cable-stayed bridges. Surface defects can lead to internal corrosion and fracturing, significantly impacting the stability of the bridge structure. The detection of surface defects from bridge cable images faces numerous challenges, including shadow disturbances due to uneven lighting and difficulties in addressing multiscale defect features. To address these challenges, this paper proposes a novel and cost-effective deep learning segmentation network, named Trans-DCN, to detect defects in the surface of the bridge cable. The network leverages an efficient Transformer-based encoder and integrates multiscale features to overcome the limitations associated with local feature inadequacy. The decoder implements an atrous Deformable Convolution (DCN) pyramid and dynamically fuses low-level feature information to perceive the complex distribution of defects. The effectiveness of Trans-DCN is evaluated by comparing it with state-of-the-art segmentation baseline models using a dataset comprising cable bridge defect images. Experimental results demonstrate that our network outperforms the state-of-the-art network SegFormer, achieving a 27.1% reduction in GFLOPs, a 1.2% increase in mean Intersection over Union, and a 1.5% increase in the F1 score. Ablation experiments confirmed the effectiveness of each module within our network, further substantiating the significant validity and advantages of Trans-DCN in the task of bridge cable defect segmentation. The network proposed in this paper provides an effective solution for downstream cable bridge image analysis. [ABSTRACT FROM AUTHOR]

The echo traces in the ionograms contain key information about the ionosphere. Therefore, the accurate extraction of these traces is crucial for the subsequent work. This paper transforms the original signal processing problem into a semantic segmentation task, combines it with the currently popular deep learning techniques, and proposes a multiscale Transformer network to achieve pixel-level trace extraction. To train the proposed model, we built a dataset by discretizing the original echo data, labeling, and other preprocessing work. A series of advanced semantic segmentation networks are utilized for comparative experiments. The analysis of the results indicates that the proposed network excels in performance, achieving the highest scores on key semantic segmentation evaluation metrics, including mIoU, Kappa, Dice, and AUC-ROC. In addition, this paper also designs a series of ablation experiments to observe the changes in network performance and to evaluate the rationality of the network design. The experimental results demonstrate the effectiveness of the network in the trace extraction task, which plays a positive role in the subsequent electron density reversal work. [ABSTRACT FROM AUTHOR]

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research. [ABSTRACT FROM AUTHOR]

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task. [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, and ISPRS Journal Of Photogrammetry And Remote Sensing. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by the USA (11.76%), France (5.88%), Spain (4%), and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most used data sources. Benchmark datasets such as ISPRS Vaihingen and Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder–decoder and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions. [ABSTRACT FROM AUTHOR]

The traditional method for retrieving cloud optical thickness (COT) is carried out through a Look-Up Table (LUT). Researchers must make a series of idealized assumptions and conduct extensive observations and record features in this scenario, consuming considerable resources. The emergence of deep learning effectively addresses the shortcomings of the traditional approach. In this paper, we first propose a daytime (SOZA < 70°) COT retrieval algorithm based on FY-4A AGRI. We establish and train a Convolutional Neural Network (CNN) model for COT retrieval, CM4CR, with the CALIPSO's COT product spatially and temporally synchronized as the ground truth. Then, a deep learning method extended from video prediction models is adopted to predict COT values based on the retrieval results obtained from CM4CR. The COT prediction model (CPM) consists of an encoder, a predictor, and a decoder. On this basis, we further incorporated a time embedding module to enhance the model's ability to learn from irregular time intervals in the input COT sequence. During the training phase, we employed Charbonnier Loss and Edge Loss to enhance the model's capability to represent COT details. Experiments indicate that our CM4CR outperforms existing COT retrieval methods, with predictions showing better performance across several metrics than other benchmark prediction models. Additionally, this paper also investigates the impact of different lengths of COT input sequences and the time intervals between adjacent frames of COT on prediction performance. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has found widespread application in SAR image object detection. However, when detecting multi-scale targets against complex backgrounds, these models often struggle to strike a balance between accuracy and speed. Furthermore, there is a continuous need to enhance the performance of current models. Hence, this paper proposes LRMSNet, a new multi-scale target detection model designed specifically for SAR images in complex backgrounds. Firstly, the paper introduces an attention module designed to enhance contextual information aggregation and capture global features, which is integrated into a backbone network with an expanded receptive field for improving SAR image feature extraction. Secondly, this paper develops an information aggregation module to effectively fuse different feature layers of the backbone network. Lastly, to better integrate feature information at various levels, this paper designs a multi-scale aggregation network. We validate the effectiveness of our method on three different SAR object detection datasets (MSAR-1.0, SSDD, and HRSID). Experimental results demonstrate that LRMSNet achieves outstanding performance with a mean average accuracy (mAP) of 95.2%, 98.9%, and 93.3% on the MSAR-1.0, SSDD, and HRSID datasets, respectively, with only 3.46 M parameters and 12.6 G floating-point operation cost (FLOPs). When compared with existing SAR object detection models on the MSAR-1.0 dataset, LRMSNet achieves state-of-the-art (SOTA) performance, showcasing its superiority in addressing SAR detection challenges in large-scale complex environments and across various object scales. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal scales over large regions. Satellite SM retrievals, especially L-band microwave remote sensing, has emerged as a feasible solution to offer spatially continuous global-scale SM information. However, the coarse spatial resolution of these L-band microwave SM retrievals poses uncertainties in many regional- and local-scale SM applications which require a high amount of spatial details. Numerous studies have been conducted to develop downscaling algorithms to enhance the spatial resolution of coarse-resolution satellite-derived SM datasets. Machine Learning (ML)-based downscaling models have gained prominence recently due to their ability to capture non-linear, complex relationships between SM and its driving factors, such as vegetation, surface temperature, topography, and climatic conditions. This review paper presents a comprehensive review of the ML-based approaches used in SM downscaling. The usage of classical, ensemble, neural nets, and deep learning methods to downscale SM products and the comparison of multiple algorithms are detailed in this paper. Insights into the significance of surface ancillary variables for model accuracy and the improvements made to ML-based SM downscaling approaches are also discussed. Overall, this paper provides useful insights for future studies on developing reliable, high-spatial-resolution SM datasets using ML-based algorithms. [ABSTRACT FROM AUTHOR]

Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone's perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research. [ABSTRACT FROM AUTHOR]

Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance. [ABSTRACT FROM AUTHOR]

With the rapid development of the modern world, it is imperative to achieve effective and efficient monitoring for territories of interest, especially for the broad ocean area. For surveillance of ship targets at sea, a common and powerful approach is to take advantage of satellite synthetic aperture radar (SAR) systems. Currently, using satellite SAR images for ship classification is a challenging issue due to complex sea situations and the imaging variances of ships. Fortunately, the emergence of advanced satellite SAR sensors has shed much light on the SAR ship automatic target recognition (ATR) task, e.g., utilizing dual-polarization (dual-pol) information to boost the performance of SAR ship classification. Therefore, in this paper we have developed a novel cross-polarimetric interaction network (CPINet) to explore the abundant polarization information of dual-pol SAR images with the help of deep learning strategies, leading to an effective solution for high-performance ship classification. First, we establish a novel multiscale deep feature extraction framework to fully mine the characteristics of dual-pol SAR images in a coarse-to-fine manner. Second, to further leverage the complementary information of dual-pol SAR images, we propose a mixed-order squeeze–excitation (MO-SE) attention mechanism, in which the first- and second-order statistics of the deep features from one single-polarized SAR image are extracted to guide the learning of another polarized one. Then, the intermediate multiscale fused and MO-SE augmented dual-polarized deep feature maps are respectively aggregated by the factorized bilinear coding (FBC) pooling method. Meanwhile, the last multiscale fused deep feature maps for each single-polarized SAR image are also individually aggregated by the FBC. Finally, four kinds of highly discriminative deep representations are obtained for loss computation and category prediction. For better network training, the gradient normalization (GradNorm) method for multitask networks is extended to adaptively balance the contribution of each loss component. Extensive experiments on the three- and five-category dual-pol SAR ship classification dataset collected from the open and free OpenSARShip database demonstrate the superiority and robustness of CPINet compared with state-of-the-art methods for the dual-polarized SAR ship classification task. [ABSTRACT FROM AUTHOR]

Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as "flickering" in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy. [ABSTRACT FROM AUTHOR]

Remote-sensing technology has gradually become one of the most important ways to extract sea–land boundaries due to its large scale, high efficiency, and low cost. However, sea–land segmentation (SLS) is still a challenging problem because of data diversity and inconsistency, "different objects with the same spectrum" or "the same object with different spectra", and noise and interference problems, etc. In this paper, a new sea–land segmentation method (PMFormer) for remote-sensing images is proposed. The contributions are mainly two points. First, based on Mask2Former architecture, we introduce the prompt mask by normalized difference water index (NDWI) of the target image and prompt encoder architecture. The prompt mask provides more reasonable constraints for attention so that the segmentation errors are alleviated in small region boundaries and small branches, which are caused by insufficiency of prior information by large data diversity or inconsistency. Second, for the large intra-class difference problem in the foreground–background segmentation in sea–land scenes, we use deep clustering to simplify the query vectors and make them more suitable for binary segmentation. Then, traditional NDWI and eight other deep-learning methods are thoroughly compared with the proposed PMFormer on three open sea–land datasets. The efficiency of the proposed method is confirmed, after the quantitative analysis, qualitative analysis, time consumption, error distribution, etc. are presented by detailed contrast experiments. [ABSTRACT FROM AUTHOR]

Water extraction from synthetic aperture radar (SAR) images has an important application value in wetland monitoring, flood monitoring, etc. However, it still faces the problems of low generalization, weak extraction ability of detailed information, and weak suppression of background noises. Therefore, a new framework, Multi-scale Attention Detailed Feature fusion Network (MADF-Net), is proposed in this paper. It comprises an encoder and a decoder. In the encoder, ResNet101 is used as a solid backbone network to capture four feature levels at different depths, and then the proposed Deep Pyramid Pool (DAPP) module is used to perform multi-scale pooling operations, which ensure that key water features can be captured even in complex backgrounds. In the decoder, a Channel Spatial Attention Module (CSAM) is proposed, which focuses on feature areas that are critical for the identification of water edges by fusing attention weights in channel and spatial dimensions. Finally, the high-level semantic information is effectively fused with the low-level edge features to achieve the final water detection results. In the experiment, Sentinel-1 SAR images of three scenes with different characteristics and scales of water body are used. The PA and IoU of water extraction by MADF-Net can reach 92.77% and 89.03%, respectively, which obviously outperform several other networks. MADF-Net carries out water extraction with high precision from SAR images with different backgrounds, which could also be used for the segmentation and classification of other tasks from SAR images. [ABSTRACT FROM AUTHOR]

Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectively. However, CNNs struggle in modeling long-term dependencies, and transformers may miss subtle spatial-spectral features. To address these challenges, this paper proposes an innovative hybrid HSI classification method aggregating hierarchical spatial-spectral features from a CNN and long pixel dependencies from a transformer. The proposed aggregation multi-hierarchical feature network (AMHFN) is designed to capture various hierarchical features and long dependencies from HSI, improving classification accuracy and efficiency. The proposed AMHFN consists of three key modules: (a) a Local-Pixel Embedding module (LPEM) for capturing prominent spatial-spectral features; (b) a Multi-Scale Convolutional Extraction (MSCE) module to capture multi-scale local spatial-spectral features and aggregate hierarchical local features; (c) a Multi-Scale Global Extraction (MSGE) module to explore multi-scale global dependencies and integrate multi-scale hierarchical global dependencies. Rigorous experiments on three public hyperspectral image (HSI) datasets demonstrated the superior performance of the proposed AMHFN method. [ABSTRACT FROM AUTHOR]

Visible and infrared image fusion is a strategy that effectively extracts and fuses information from different sources. However, most existing methods largely neglect the issue of lighting imbalance, which makes the same fusion models inapplicable to different scenes. Several methods obtain low-level features from visible and infrared images at an early stage of input or shallow feature extraction. However, these methods do not explore how low-level features provide a foundation for recognizing and utilizing the complementarity and common information between the two types of images. As a result, the complementarity and common information between the images is not fully analyzed and discussed. To address these issues, we propose a Self-Attention Progressive Network for the fusion of infrared and visible images in this paper. Firstly, we construct a Lighting-Aware Sub-Network to analyze lighting distribution, and introduce intensity loss to measure the probability of scene illumination. This approach enhances the model's adaptability to lighting conditions. Secondly, we introduce self-attention learning to design a multi-state joint feature extraction module (MSJFEM) that fully utilizes the contextual information among input keys. It guides the learning of a dynamic attention matrix to strengthen the capacity for visual representation. Finally, we design a Difference-Aware Propagation Module (DAPM) to extract and integrate edge details from the source images while supplementing differential information. The experiments across three benchmark datasets reveal that the proposed approach exhibits satisfactory performance compared to existing methods. [ABSTRACT FROM AUTHOR]

Human settlement areas significantly impact the environment, leading to changes in both natural and built environments. Comprehensive information on human settlements, particularly in urban areas, is crucial for effective sustainable development planning. However, urban land use investigations are often limited to two-dimensional building footprint maps, neglecting the three-dimensional aspect of building structures. This paper addresses this issue to contribute to Sustainable Development Goal 11, which focuses on making human settlements inclusive, safe, and sustainable. In this study, Sentinel-1 data are used as the primary source to estimate building heights. One challenge addressed is the issue of multiple backscattering in Sentinel-1's signal, particularly in densely populated areas with high-rise buildings. To mitigate this, firstly, Sentinel-1 data from different directions, orbit paths, and polarizations are utilized. Combining ascending and descending orbits significantly improves estimation accuracy, and incorporating a higher number of paths provides additional information. However, Sentinel-1 data alone are not sufficiently rich at a global scale across different orbits and polarizations. Secondly, to enhance the accuracy further, Sentinel-1 data are corrected using nighttime light data as additional information, which shows promising results in addressing multiple backscattering issues. Finally, a deep learning model is trained to generate building height maps using these features, achieving a mean absolute error of around 2 m and a mean square error of approximately 13. The generalizability of this method is demonstrated in several cities with diverse built-up structures, including London, Berlin, and others. Finally, a building height map of Iran is generated and evaluated against surveyed buildings, showcasing its large-scale mapping capability. [ABSTRACT FROM AUTHOR]

The following paper focuses on evaluating the quality of image prediction in the context of searching for plants of a single species, using the example of Heracleum sosnowskyi Manden, in a given area. This process involves a simplified classification that ends with a segmentation step. Because of the particular characteristics of environmental data, such as large areas of plant occurrence, significant partitioning of the population, or characteristics of a single individual, the use of standard statistical measures such as Accuracy, the Jaccard Index, or Dice Coefficient does not produce reliable results, as shown later in this study. This issue demonstrates the need for a new method for assessing the betted prediction quality adapted to the unique characteristics of vegetation patch detection. The main aim of this study is to provide such a metric and demonstrate its usefulness in the cases discussed. Our proposed metric introduces two new coefficients, M + and M − , which, respectively, reward true positive regions and penalise false positive regions, thus providing a more nuanced assessment of segmentation quality. The effectiveness of this metric has been demonstrated in different scenarios focusing on variations in spatial distribution and fragmentation of theoretical vegetation patches, comparing the proposed new method with traditional metrics. The results indicate that our metric offers a more flexible and accurate assessment of segmentation quality, especially in cases involving complex environmental data. This study aims to demonstrate the usefulness and applicability of the metric in real-world vegetation patch detection tasks. [ABSTRACT FROM AUTHOR]

Due to the continuous degradation of onboard satellite instruments over time, satellite images undergo degradation, necessitating calibration for tasks reliant on satellite data. The previous relative radiometric calibration methods are mainly categorized into traditional methods and deep learning methods. The traditional methods involve complex computations for each calibration, while deep-learning-based approaches tend to oversimplify the calibration process, utilizing generic computer vision models without tailored structures for calibration tasks. In this paper, we address the unique challenges of calibration by introducing a novel approach: a multi-task convolutional neural network calibration model leveraging temporal information. This pioneering method is the first to integrate temporal dynamics into the architecture of neural network calibration models. Extensive experiments conducted on the FY3A/B/C VIRR datasets showcase the superior performance of our approach compared to the existing state-of-the-art traditional and deep learning methods. Furthermore, tests with various backbones confirm the broad applicability of our framework across different convolutional neural networks. [ABSTRACT FROM AUTHOR]

Underwater acoustic target recognition has always played a pivotal role in ocean remote sensing. By analyzing and processing ship-radiated signals, it is possible to determine the type and nature of a target. Historically, traditional signal processing techniques have been employed for target recognition in underwater environments, which often exhibit limitations in accuracy and efficiency. In response to these limitations, the integration of artificial intelligence (AI) methods, particularly those leveraging machine learning and deep learning, has attracted increasing attention in recent years. Compared to traditional methods, these intelligent recognition techniques can autonomously, efficiently, and accurately identify underwater targets. This paper comprehensively reviews the contributions of intelligent techniques in underwater acoustic target recognition and outlines potential future directions, offering a forward-looking perspective on how ongoing advancements in AI can further revolutionize underwater acoustic target recognition in ocean remote sensing. [ABSTRACT FROM AUTHOR]

Snow detection is imperative in remote sensing for various applications, including climate change monitoring, water resources management, and disaster warning. Recognizing the limitations of current deep learning algorithms in cloud and snow boundary segmentation, as well as issues like detail snow information loss and mountainous snow omission, this paper presents a novel snow detection network based on Swin-Transformer and U-shaped dual-branch encoder structure with geographic information (SD-GeoSTUNet), aiming to address the above issues. Initially, the SD-GeoSTUNet incorporates the CNN branch and Swin-Transformer branch to extract features in parallel and the Feature Aggregation Module (FAM) is designed to facilitate the detail feature aggregation via two branches. Simultaneously, an Edge-enhanced Convolution (EeConv) is introduced to promote snow boundary contour extraction in the CNN branch. In particular, auxiliary geographic information, including altitude, longitude, latitude, slope, and aspect, is encoded in the Swin-Transformer branch to enhance snow detection in mountainous regions. Experiments conducted on Levir_CS, a large-scale cloud and snow dataset originating from Gaofen-1, demonstrate that SD-GeoSTUNet achieves optimal performance with the values of 78.08%, 85.07%, and 92.89% for I o U _ s , F 1 _ s , and M P A , respectively, leading to superior cloud and snow boundary segmentation and thin cloud and snow detection. Further, ablation experiments reveal that integrating slope and aspect information effectively alleviates the omission of snow detection in mountainous areas and significantly exhibits the best vision under complex terrain. The proposed model can be used for remote sensing data with geographic information to achieve more accurate snow extraction, which is conducive to promoting the research of hydrology and agriculture with different geospatial characteristics. [ABSTRACT FROM AUTHOR]

Applying deep learning algorithms in the construction industry holds tremendous potential for enhancing site management, safety, and efficiency. The development of such algorithms necessitates a comprehensive and diverse image dataset. This study introduces the Aerial Image Dataset for Construction (AIDCON), a novel aerial image collection containing 9563 construction machines across nine categories annotated at the pixel level, carrying critical value for researchers and professionals seeking to develop and refine object detection and segmentation algorithms across various construction projects. The study highlights the benefits of utilizing UAV-captured images by evaluating the performance of five cutting-edge deep learning algorithms—Mask R-CNN, Cascade Mask R-CNN, Mask Scoring R-CNN, Hybrid Task Cascade, and Pointrend—on the AIDCON dataset. It underscores the significance of clustering strategies for generating reliable and robust outcomes. The AIDCON dataset's unique aerial perspective aids in reducing occlusions and provides comprehensive site overviews, facilitating better object positioning and segmentation. The findings presented in this paper have far-reaching implications for the construction industry, as they enhance construction site efficiency while setting the stage for future advancements in construction site monitoring and management utilizing remote sensing technologies. [ABSTRACT FROM AUTHOR]

Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers have adopted 3D point cloud technology for organ-level segmentation, extending beyond manual and 2D visual measurement methods. However, analyzing plant phenotypic traits using 3D point cloud technology is influenced by various factors such as data acquisition environment, sensors, research subjects, and model selection. Although the existing literature has summarized the application of this technology in plant phenotyping, there has been a lack of in-depth comparison and analysis at the algorithm model level. This paper evaluates the segmentation performance of various deep learning models on point clouds collected or generated under different scenarios. These methods include outdoor real planting scenarios and indoor controlled environments, employing both active and passive acquisition methods. Nine classical point cloud segmentation models were comprehensively evaluated: PointNet, PointNet++, PointMLP, DGCNN, PointCNN, PAConv, CurveNet, Point Transformer (PT), and Stratified Transformer (ST). The results indicate that ST achieved optimal performance across almost all environments and sensors, albeit at a significant computational cost. The transformer architecture for points has demonstrated considerable advantages over traditional feature extractors by accommodating features over longer ranges. Additionally, PAConv constructs weight matrices in a data-driven manner, enabling better adaptation to various scales of plant organs. Finally, a thorough analysis and discussion of the models were conducted from multiple perspectives, including model construction, data collection environments, and platforms. [ABSTRACT FROM AUTHOR]

The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR imagery can significantly reduce the time and computational resources required for complete SAR imaging, enabling lightweight real-time ship detection methods to be implemented on an airborne or spaceborne SAR platform. However, there is a lack of lightweight ship detection methods specifically designed for the SAR range-compressed domain. In this paper, we propose Fast Range-Compressed Detection (FastRCDet), a novel lightweight network for ship detection in the SAR range-compressed domain. Firstly, to address the distinctive geometric characteristics of the SAR range-compressed domain, we propose a Lightweight Adaptive Network (LANet) as the backbone of the network. We introduce Arbitrary Kernel Convolution (AKConv) as a fundamental component, which enables the flexible adjustment of the receptive field shape and better adaptation to the large scale and aspect ratio characteristics of ships in the range-compressed domain. Secondly, to enhance the efficiency and simplicity of the network model further, we propose an innovative Multi-Scale Fusion Head (MSFH) module directly integrated after the backbone, eliminating the need for a neck module. This module effectively integrates features at various scales to more accurately capture detailed information about the target. Thirdly, to further enhance the network's adaptability to ships in the range-compressed domain, we propose a novel Direction IoU (DIoU) loss function that leverages angle cost to control the convergence direction of predicted bounding boxes, thereby improving detection accuracy. Experimental results on a publicly available dataset demonstrate that FastRCDet achieves significant reductions in parameters and computational complexity compared to mainstream networks without compromising detection performance in SAR range-compressed images. FastRCDet achieves a low parameter of 2.49 M and a high detection speed of 38.02 frames per second (FPS), surpassing existing lightweight detection methods in terms of both model size and processing rate. Simultaneously, it attains an average accuracy (AP) of 77.12% in terms of its detection performance. This method provides a baseline in lightweight network design for SAR ship detection in the range-compressed domain and offers practical implications for resource-constrained embedded platforms. [ABSTRACT FROM AUTHOR]

The transmission of high-frequency signals over long distances depends on the ionosphere's reflective properties, with the selection of operating frequencies being closely tied to variations in the ionosphere. The accurate prediction of ionospheric critical frequency foF2 and other parameters in low latitudes is of great significance for understanding ionospheric changes in high-frequency communications. Currently, deep learning algorithms demonstrate significant advantages in capturing characteristics of the ionosphere. In this paper, a state-of-the-art hybrid neural network is utilized in conjunction with a temporal pattern attention mechanism for predicting variations in the foF2 parameter during high- and low-solar activity years. Convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM), which is capable of extracting spatiotemporal features of ionospheric variations, are incorporated into a hybrid neural network. The foF2 data used for training and testing come from three observatories in Brisbane (27°53′S, 152°92′E), Darwin (12°45′S, 130°95′E) and Townsville (19°63′S, 146°85′E) in 2000, 2008, 2009 and 2014 (the peak or trough years of solar activity in solar cycles 23 and 24), using the advanced Australian Digital Ionospheric Sounder. The results show that the proposed model accurately captures the changes in ionospheric foF2 characteristics and outperforms International Reference Ionosphere 2020 (IRI-2020) and BiLSTM ionospheric prediction models. [ABSTRACT FROM AUTHOR]

In developing modern intelligent transportation systems, integrated sensing and communication (ISAC) technology has become an efficient and promising method for vehicle road services. To enhance traffic safety and efficiency through real-time interaction between vehicles and roads, this paper proposes a searching–deciding scheme for an alternation radar-communication (radar-comm) system. Firstly, its communication performance is derived for a given detection probability. Then, we process the echo data from real-world millimeter-wave (mmWave) radar into four-dimensional (4D) point cloud datasets and thus separate different hybrid modes of single-vehicle and vehicle fleets into three types of scenes. Based on these datasets, an efficient labeling method is proposed to assist accurate vehicle target detection. Finally, a novel vehicle detection scheme is proposed to classify various scenes and accurately detect vehicle targets based on deep learning methods. Extensive experiments on collected real-world datasets demonstrate that compared to benchmarks, the proposed scheme obtains substantial radar performance and achieves competitive communication performance. [ABSTRACT FROM AUTHOR]

Automatic classification of buildings within port areas from synthetic aperture radar (SAR) images is crucial for effective port monitoring and planning. Yet, the unique challenges of SAR imaging, such as side-looking geometry, multi-bouncing scattering, and the compact arrangement of structures, often lead to incomplete building structures and blurred boundaries in classification results. To address these issues, this paper introduces SPformer, an efficient and lightweight pyramid transformer model tailored for semantic segmentation. The SPformer utilizes a pyramid transformer encoder with spatially separable self-attention (SSSA) to refine both local and global spatial information and to process multi-scale features, enhancing the accuracy of building structure delineation. It also integrates a lightweight all multi-layer perceptron (ALL-MLP) decoder to consolidate multi-scale information across various depths and attention scopes, refining detail processing. Experimental results on the Gaofen-3 (GF-3) 1 m port building classification dataset demonstrate the effectiveness of SPformer, achieving competitive performance compared to state-of-the-art models, with mean intersection over union (mIoU) and mean F1-score (mF1) reaching 77.14% and 87.04%, respectively, while maintaining a compact model size and lower computational requirements. Experiments conducted on the entire scene of SAR images covering port area also show the good capabilities of the proposed method. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has made remarkable progress in recent years in remote sensing applications, including environmental monitoring, crisis management, city planning, and agriculture. However, the critical challenge in utilizing AI models in real-world remote sensing applications is maintaining their robustness and reliability, particularly against adversarial attacks. In adversarial attacks, attackers manipulate benign data to create a perturbation to mislead AI models into predicting incorrect decisions, posing a catastrophic threat to the security of their applications, particularly in crucial decision-making contexts. These attacks pose a significant threat to the integrity and comprehensiveness of AI models in remote sensing applications, as they can lead to inaccurate decisions with substantial consequences. In this paper, we propose to develop an adversarial robustness technique that will ensure the AI model's accurate prediction in the presence of adversarial perturbation. In this work, we address these challenges by developing a better adversarial training approach using explainable AI method-guided features and data augmentation techniques to strengthen the AI model prediction in remote sensing data against adversarial attacks. The proposed approach achieved the best adversarial robustness against Project Gradient Descent (PGD) attacks in EuroSAT and AID datasets and showed transferability of robustness against unseen attacks. [ABSTRACT FROM AUTHOR]

The SAR system possesses the ability to carry out all-day and all-weather imaging, which is highly valuable in the application of aircraft identification. However, aircraft identification from SAR images still faces great challenges due to speckle noise interference, multiscale problems, and complex background interference. To solve these problems, an efficient bidirectional path multiscale fusion and attention network (EBMA-Net) is proposed in this paper. It employs bidirectional connectivity to fuse the features of aircraft with different scales to perform the accurate detection of aircraft even when the background is highly complex. In the presented EBMA-Net, a module called efficient multiscale channel attention fusion (EMCA) and three parallel squeeze efficient channel attention (SECA) modules are proposed. In the EMCA module, the bidirectional paths are created by stacking upper and lower fusion modules, which effectively integrate shallow detailed features and deep semantic information. So, the detection performance of aircraft at different scales is improved. In the SECA module, the dependency relationship between feature channels is explicitly modeled, which can automatically learn the importance of different channels, prioritize key features, so as to improve the precision and robustness of aircraft identification. In the experiment, the public dataset of aircraft identification (i.e., SAR-AIRcraft-1.0, which is generated from the GF-3 satellite) from high-resolution SAR systems is used, and several other excellent target-detection networks are used for performance comparison, namely, YOLOv5s, YOLOv7, MGCAN, and EBPA2N. According to the results, the average aircraft detection accuracy of EBMA-Net is 91.31%, which is 4.5% higher than YOLOv7; and the false alarm rate is decreased by 5%. Its accuracy in the identification of aircraft can reach 95.6%, which is about 3.7% higher than YOLOv7. Therefore, the EBMA-Net obviously outperforms the other networks for aircraft detection and identification. The proposed EBMA-Net, which can capture the detailed information and better restrain the background interference, could also be used to perform the detection and identification of dense targets with different scales and background from SAR images. [ABSTRACT FROM AUTHOR]

Applying deep learning to high-precision ionospheric parameter prediction is a significant and growing field within the realm of space weather research. This paper proposes an improved model, Mixed Convolutional Neural Network (CNN)—Bidirectional Long Short-Term Memory (BiLSTM), for predicting the Total Electron Content (TEC) in China. This model was trained using the longest available Global Ionospheric Maps (GIM)-TEC from 1998 to 2023 in China, and underwent an interpretability analysis and accuracy evaluation. The results indicate that historical TEC maps play the most critical role, followed by Kp, ap, AE, F10.7, and time factor. The contributions of Dst and Disturbance Index (DI) to improving accuracy are relatively small but still essential. In long-term predictions, the contributions of the geomagnetic index, solar activity index, and time factor are higher. In addition, the model performs well in short-term predictions, accurately capturing the occurrence, evolution, and classification of ionospheric storms. However, as the predicted length increases, the accuracy gradually decreases, and some erroneous predictions may occur. The northeast region exhibits lower accuracy but a higher F1 score, which may be attributed to the frequency of ionospheric storm occurrences in different locations. Overall, the model effectively predicts the trends and evolution processes of ionospheric storms. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) aims to decompose mixed pixels into a set of endmembers and corresponding abundances. Deep learning-based HU methods are currently a hot research topic, but most existing unmixing methods still rely on per-pixel training or employ convolutional neural networks (CNNs), which overlook the non-local correlations of materials and spectral characteristics. Furthermore, current research mainly focuses on linear mixing models, which limits the feature extraction capability of deep encoders and further improvement in unmixing accuracy. In this paper, we propose a nonlinear unmixing network capable of extracting global spatial-spectral features. The network is designed based on an autoencoder architecture, where a dual-stream CNNs is employed in the encoder to separately extract spectral and local spatial information. The extracted features are then fused together to form a more complete representation of the input data. Subsequently, a linear projection-based multi-head self-attention mechanism is applied to capture global contextual information, allowing for comprehensive spatial information extraction while maintaining lightweight computation. To achieve better reconstruction performance, a model-free nonlinear mixing approach is adopted to enhance the model's universality, with the mixing model learned entirely from the data. Additionally, an initialization method based on endmember bundles is utilized to reduce interference from outliers and noise. Comparative results on real datasets against several state-of-the-art unmixing methods demonstrate the superior of the proposed approach. [ABSTRACT FROM AUTHOR]

Earth observation missions such as Sentinel and Landsat support the large-scale identification of agricultural crops by providing free radar and multispectral satellite images. The extraction of representative image information as well as the combination of different image sources for improved feature selection still represent a major challenge in the field of remote sensing. In this paper, we propose a novel three-dimensional (3D) deep learning U-Net model to fuse multi-level image features from multispectral and synthetic aperture radar (SAR) time series data for seasonal crop-type mapping at a regional scale. For this purpose, we used a dual-stream U-Net with a 3D squeeze-and-excitation fusion module applied at multiple stages in the network to progressively extract and combine multispectral and SAR image features. Additionally, we introduced a distinctive method for generating patch-based multitemporal multispectral composites by selective image sampling within a 14-day window, prioritizing those with minimal cloud cover. The classification results showed that the proposed network provided the best overall accuracy (94.5%) compared to conventional two-dimensional (2D) and three-dimensional U-Net models (2D: 92.6% and 3D: 94.2%). Our network successfully learned multi-modal dependencies between the multispectral and SAR satellite images, leading to improved field mapping of spectrally similar and heterogeneous classes while mitigating the limitations imposed by persistent cloud coverage. Additionally, the feature representations extracted by the proposed network demonstrated their transferability to a new cropping season, providing a reliable mapping of spatio-temporal crop type patterns. [ABSTRACT FROM AUTHOR]

In autonomous driving, Frequency-Modulated Continuous-Wave (FMCW) radar has gained widespread acceptance for target detection due to its resilience and dependability under diverse weather and illumination circumstances. Although deep learning radar target identification models have seen fast improvement, there is a lack of research on their susceptibility to adversarial attacks. Various spoofing attack techniques have been suggested to target radar sensors by deliberately sending certain signals through specialized devices. In this paper, we proposed a new adversarial deep learning network for spoofing attacks in radar target detection (RTD). Multi-level adversarial attack prevention using deep learning is designed for the coherence pulse deep feature map from DAALnet and Range-Doppler (RD) map from TDDLnet. After the discrimination of the attack, optimization of hybrid deep learning (OHDL) integrated with enhanced PSO is used to predict the range and velocity of the target. Simulations are performed to evaluate the sensitivity of AOHDL for different radar environment configurations. RMSE of AOHDL is almost the same as OHDL without attack conditions and it outperforms the earlier RTD implementations. [ABSTRACT FROM AUTHOR]

This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution and research publication trends, (3) progress of remote sensing and learning algorithms, and (4) application of remote sensing techniques and learning models for landslide susceptibility mapping, detections, prediction, inventory and deformation monitoring, assessment, and extraction and management. The literature selections were based on keyword searches using title/abstract and keywords from Web of Science and Scopus. A total of 186 research articles published between 2011 and 2024 were critically reviewed to provide answers to research questions related to the recent advances in the use of remote sensing technologies combined with artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms. The review revealed that these methods have high efficiency in landslide detection, prediction, monitoring, and hazard mapping. A few current issues were also identified and discussed. [ABSTRACT FROM AUTHOR]

Cracks are a common defect in civil infrastructures, and their occurrence is often closely related to structural loading conditions, material properties, design and construction, and other factors. Therefore, detecting and analyzing cracks in civil infrastructures can effectively determine the extent of damage, which is crucial for safe operation. In this paper, Web of Science (WOS) and Google Scholar were used as literature search tools and "crack", "civil infrastructure", and "computer vision" were selected as search terms. With the keyword "computer vision", 325 relevant documents were found in the study period from 2020 to 2024. A total of 325 documents were searched again and matched with the keywords, and 120 documents were selected for analysis and research. Based on the main research methods of the 120 documents, we classify them into three crack detection methods: fusion of traditional methods and deep learning, multimodal data fusion, and semantic image understanding. We examine the application characteristics of each method in crack detection and discuss its advantages, challenges, and future development trends. [ABSTRACT FROM AUTHOR]

Pansharpening, a pivotal task in remote sensing, involves integrating low-resolution multispectral images with high-resolution panchromatic images to synthesize an image that is both high-resolution and retains multispectral information. These pansharpened images enhance precision in land cover classification, change detection, and environmental monitoring within remote sensing data analysis. While deep learning techniques have shown significant success in pansharpening, existing methods often face limitations in their evaluation, focusing on restricted satellite data sources, single scene types, and low-resolution images. This paper addresses this gap by introducing PanBench, a high-resolution multi-scene dataset containing all mainstream satellites and comprising 5898 pairs of samples. Each pair includes a four-channel (RGB + near-infrared) multispectral image of 256 × 256 pixels and a mono-channel panchromatic image of 1024 × 1024 pixels. To avoid irreversible loss of spectral information and achieve a high-fidelity synthesis, we propose a Cascaded Multiscale Fusion Network (CMFNet) for pansharpening. Multispectral images are progressively upsampled while panchromatic images are downsampled. Corresponding multispectral features and panchromatic features at the same scale are then fused in a cascaded manner to obtain more robust features. Extensive experiments validate the effectiveness of CMFNet. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of different despeckling filters applied on both synthetically corrupted optical images and actual Synthetic Aperture Radar (SAR) images. Several authors use optical images as ground truth and then the images are corrupted by using a Gamma model to simulate the speckle, while other approaches use methods like multitemporal fusion to generate a ground truth using actual SAR images, which provides a result somehow equivalent to the one from the common multi look technique. Well-known filters, like local, and non-local and some of them based on artificial intelligence and deep learning, are applied to these two types of images and their performance is assessed by a quantitative analysis. One last validation is performed with a newly proposed method by using ratio images, resulting from the mathematical division (Hadamard division) of filtered and noisy images, to measure how similar the initial and the remaining speckle are by considering its Gamma distribution and divergence measurement. Our findings suggest that despeckling models relying on artificial intelligence exhibit notable efficiency, albeit concurrently displaying inflexibility when applied to particular image types based on the training dataset. Additionally, our experiments underscore the utility of the divergence measurement in ratio images in facilitating both visual inspection and quantitative evaluation of residual speckles within the filtered images. [ABSTRACT FROM AUTHOR]

In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. [ABSTRACT FROM AUTHOR]

Given the propagation characteristics of sound waves and the complexity of the underwater environment, denoising forward-looking sonar image data presents a formidable challenge. Existing studies often add noise to sonar images and then explore methods for its removal. This approach neglects the inherent complex noise in sonar images, resulting in inaccurate evaluations of traditional denoising methods and poor learning of noise characteristics by deep learning models. To address the lack of high-quality data for FLS denoising model training, we propose a simulation algorithm for forward-looking sonar data based on RGBD data. By utilizing rendering techniques and noise simulation algorithms, high-quality noise-free and noisy sonar data can be rapidly generated from existing RGBD data. Based on these data, we optimize the loss function and training process of the FLS denoising model, achieving significant improvements in noise removal and feature preservation compared to other methods. Finally, this paper performs both qualitative and quantitative analyses of the algorithm's performance using real and simulated sonar data. Compared to the latest FLS denoising models based on traditional methods and deep learning techniques, our method demonstrates significant advantages in denoising capability. All inference results for the Marine Debris Dataset (MDD) have been made open source, facilitating subsequent research and comparison. [ABSTRACT FROM AUTHOR]

Site-specific weed management employs image data to generate maps through various methodologies that classify pixels corresponding to crop, soil, and weed. Further, many studies have focused on identifying specific weed species using spectral data. Nonetheless, the availability of open-access weed datasets remains limited. Remarkably, despite the extensive research employing hyperspectral imaging data to classify species under varying conditions, to the best of our knowledge, there are no open-access hyperspectral weed datasets. Consequently, accessible spectral weed datasets are primarily RGB or multispectral and mostly lack the temporal aspect, i.e., they contain a single measurement day. This paper introduces an open dataset for training and evaluating machine-learning methods and spectral features to classify weeds based on various biological traits. The dataset comprises 30 hyperspectral images, each containing thousands of pixels with 204 unique visible and near-infrared bands captured in a controlled environment. In addition, each scene includes a corresponding RGB image with a higher spatial resolution. We included three weed species in this dataset, representing different botanical groups and photosynthetic mechanisms. In addition, the dataset contains meticulously sampled labeled data for training and testing. The images represent a time series of the weed's growth along its early stages, critical for precise herbicide application. We conducted an experimental evaluation to test the performance of a machine-learning approach, a deep-learning approach, and Spectral Mixture Analysis (SMA) to identify the different weed traits. In addition, we analyzed the importance of features using the random forest algorithm and evaluated the performance of the selected algorithms while using different sets of features. [ABSTRACT FROM AUTHOR]

Ship detection from synthetic aperture radar (SAR) imagery is crucial for various fields in real-world applications. Numerous deep learning-based detectors have been investigated for SAR ship detection, which requires a substantial amount of labeled data for training. However, SAR data annotation is time-consuming and demands specialized expertise, resulting in deep learning-based SAR ship detectors struggling due to a lack of annotations. With limited labeled data, semi-supervised learning is a popular approach for boosting detection performance by excavating valuable information from unlabeled data. In this paper, a semi-supervised SAR ship detection network is proposed, termed a Multi-Teacher Dempster-Shafer Evidence Fusion Net-work (MTDSEFN). The MTDSEFN is an enhanced framework based on the basic teacher–student skeleton frame, comprising two branches: the Teacher Group (TG) and the Agency Teacher (AT). The TG utilizes multiple teachers to generate pseudo-labels for different augmentation versions of unlabeled samples, which are then refined to obtain high-quality pseudo-labels by using Dempster-Shafer (D-S) fusion. The AT not only serves to deliver weights of its own teacher to the TG at the end of each epoch but also updates its own weights after each iteration, enabling the model to effectively learn rich information from unlabeled data. The combination of TG and AT guarantees both reliable pseudo-label generation and a comprehensive diversity of learning information from numerous unlabeled samples. Extensive experiments were performed on two public SAR ship datasets, and the results demonstrated the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model's performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~ 7.3 × 10 − 4 km−1sr−1, which is lower than the CALIOP 1064 nm night MDB value of 8.6 × 10 − 4 km−1sr−1. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products. [ABSTRACT FROM AUTHOR]

For modern radar systems, small unmanned aerial vehicles (UAVs) belong to a typical types of targets with 'low, slow, and small' characteristics. In complex combat environments, the functional requirements of radar systems are not only limited to achieving stable detection and tracking performance but also to effectively complete the recognition of small UAV targets. In this paper, a multi-dimensional feature fusion framework for small UAV target recognition utilizing a small-sized and low-cost high-resolution radar is proposed, which can fully extract and combine the geometric structure features and the micro-motion features of small UAV targets. For the performance analysis, the echo data of different small UAV targets was measured and collected with a millimeter-wave radar, and the dataset consists of high-resolution range profiles (HRRP) and micro-Doppler time–frequency spectrograms was constructed for training and testing. The effectiveness of the proposed method was demonstrated by a series of comparison experiments, and the overall accuracy of the proposed method can reach 98.5%, which demonstrates that the proposed multi-dimensional feature fusion method can achieve better recognition performance than that of classical algorithms and higher robustness than that of single features for small UAV targets. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue on advanced machine learning and deep learning techniques for remote sensing. The issue includes 16 research papers that cover a range of topics, including hyperspectral image classification, moving point target detection, radar echo extrapolation, and remote sensing object detection. Each paper introduces a novel approach or model and provides extensive testing and evaluation to demonstrate its effectiveness. The insights shared in this special issue are expected to contribute to future advancements in artificial intelligence-based remote sensing research. [Extracted from the article]

The census and management of hazard-bearing entities, along with the integrity of data quality, form crucial foundations for disaster risk assessment and zoning. By addressing the challenge of feature confusion, prevalent in single remotely sensed image recognition methods, this paper introduces a novel method, Spatially Constrained Deep Learning (SCDL), that combines deep learning with spatial constraint strategies for the extraction of disaster-bearing bodies, focusing on dams as a typical example. The methodology involves the creation of a dam dataset using a database of dams, followed by the training of YOLOv5, Varifocal Net, Faster R-CNN, and Cascade R-CNN models. These models are trained separately, and highly confidential dam location information is extracted through parameter thresholding. Furthermore, three spatial constraint strategies are employed to mitigate the impact of other factors, particularly confusing features, in the background region. To assess the method's applicability and efficiency, Qinghai Province serves as the experimental area, with dam images from the Google Earth Pro database used as validation samples. The experimental results demonstrate that the recognition accuracy of SCDL reaches 94.73%, effectively addressing interference from background factors. Notably, the proposed method identifies six dams not recorded in the GOODD database, while also detecting six dams in the database that were previously unrecorded. Additionally, four dams misdirected in the database are corrected, contributing to the enhancement and supplementation of the global dam geo-reference database and providing robust support for disaster risk assessment. In conclusion, leveraging open geographic data products, the comprehensive framework presented in this paper, encompassing deep learning target detection technology and spatial constraint strategies, enables more efficient and accurate intelligent retrieval of disaster-bearing bodies, specifically dams. The findings offer valuable insights and inspiration for future advancements in related fields. [ABSTRACT FROM AUTHOR]