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Digital photogrammetry has attracted widespread attention in the field of geotechnical and geological surveys due to its low-cost, ease of use, and contactless mode. In this work, with the purpose of studying the progressive block surficial detachments of a landslide, we developed a monitoring system based on fixed multi-view time-lapse cameras. Thanks to a newly developed photogrammetric algorithm based on the comparison of photo sequences through a structural similarity metric and the computation of the disparity map of two convergent views, we can quickly detect the occurrence of collapse events, determine their location, and calculate the collapse volume. With the field data obtained at the Perarolo landslide site (Belluno Province, Italy), we conducted preliminary tests of the effectiveness of the algorithm and its accuracy in the volume calculation. The method of quickly and automatically obtaining the collapse information proposed in this paper can extend the potential of landslide monitoring systems based on videos or photo sequence and it will be of great significance for further research on the link between the frequency of collapse events and the driving factors. [ABSTRACT FROM AUTHOR]

Semantic segmentation is currently a hot topic in remote sensing image processing. There are extensive applications in land planning and surveying. Many current studies combine Convolutional Neural Networks (CNNs), which extract local information, with Transformers, which capture global information, to obtain richer information. However, the fused feature information is not sufficiently enriched and it often lacks detailed refinement. To address this issue, we propose a novel method called the Multi-View Feature Fusion and Rich Information Refinement Network (MFRNet). Our model is equipped with the Multi-View Feature Fusion Block (MAFF) to merge various types of information, including local, non-local, channel, and positional information. Within MAFF, we introduce two innovative methods. The Sliding Heterogeneous Multi-Head Attention (SHMA) extracts local, non-local, and positional information using a sliding window, while the Multi-Scale Hierarchical Compressed Channel Attention (MSCA) leverages bar-shaped pooling kernels and stepwise compression to obtain reliable channel information. Additionally, we introduce the Efficient Feature Refinement Module (EFRM), which enhances segmentation accuracy by interacting the results of the Long-Range Information Perception Branch and the Local Semantic Information Perception Branch. We evaluate our model on the ISPRS Vaihingen and Potsdam datasets. We conducted extensive comparison experiments with state-of-the-art models and verified that MFRNet outperforms other models. [ABSTRACT FROM AUTHOR]

This study is structured to address the problem of mapping the bottom of shallow and ultra-shallow inland water bodies using high-resolution satellite imagery. These environments, with their diverse distribution of optically relevant components, pose a challenge to traditional mapping methods. The study was conducted on several research issues, each focusing on a specific aspect of the SDB, related to the selection of spectral bands and regression models, regression models creation, evaluation of the influence of the number and spatial distribution of reference soundings, and assessment of the quality of the bathymetric surface, with a focus on microtopography. The study utilized basic empirical techniques, incorporating high-precision reference data acquired via an unmanned surface vessel (USV) integrated with a single-beam echosounder (SBES), and Global Navigation Satellite System (GNSS) receiver measurements. The performed investigation allowed the optimization of a methodology for bathymetry acquisition of such areas by identifying the impact of individual processing components. The first results indicated the usefulness of the proposed approach, which can be confirmed by the values of the obtained RMS errors of elaborated bathymetric surfaces in the range of up to several centimeters in some study cases. The work also points to the problematic nature of this type of study, which can contribute to further research into the application of remote sensing techniques for bathymetry, especially during acquisition in optically complex waters. [ABSTRACT FROM AUTHOR]

Built heritage materials assessment is an important task for planning and managing future conservation works. The uniqueness of each historical building makes reconnaissance operations more complex and specific for every single building. In the past, visual inspection and invasive techniques were widely used to investigate surface materials. Non-destructive techniques (NDTs) such as multi-band photogrammetry and remote sensing can help to assess the buildings without any contact with the investigated objects, restricting the disruptive tests on limited areas and reducing the testing time and costs of the surveys. This paper presents the results obtained using multi-band images acquired with a low-cost imaging solution after interchanging several filters, and the application of the principal components analysis (PCA) to recognize different materials of a significant historical monument. The Serranos Towers, built between 1392 and 1398, suffered several interventions in the past that affected their state of conservation with the replacement of different materials. The results of the study show the usefulness of applying PCA to distinguish different surface materials, often similar to the original ones, in a fast and efficient way to investigate and analyze our heritage legacy. [ABSTRACT FROM AUTHOR]

In view of the complexity and diversity of hyperspectral images (HSIs), the classification task has been a major challenge in the field of remote sensing image processing. Hyperspectral classification (HSIC) methods based on neural architecture search (NAS) is a current attractive frontier that not only automatically searches for neural network architectures best suited to the characteristics of HSI data, but also avoids the possible limitations of manual design of neural networks when dealing with new classification tasks. However, the existing NAS-based HSIC methods have the following limitations: (1) the search space lacks efficient convolution operators that can fully extract discriminative spatial–spectral features, and (2) NAS based on traditional differentiable architecture search (DARTS) has performance collapse caused by unfair competition. To overcome these limitations, we proposed a neural architecture search method with receptive field spatial–spectral attention (RFSS-NAS), which is specifically designed to automatically search the optimal architecture for HSIC. Considering the core needs of the model in extracting more discriminative spatial–spectral features, we designed a novel and efficient attention search space. The core component of this innovative space is the receptive field spatial–spectral attention convolution operator, which is capable of precisely focusing on the critical information in the image, thus greatly enhancing the quality of feature extraction. Meanwhile, for the purpose of solving the unfair competition issue in the traditional differentiable architecture search (DARTS) strategy, we skillfully introduce the Noisy-DARTS strategy. The strategy ensures the fairness and efficiency of the search process and effectively avoids the risk of performance crash. In addition, to further improve the robustness of the model and ability to recognize difficult-to-classify samples, we proposed a fusion loss function by combining the advantages of the label smoothing loss and the polynomial expansion perspective loss function, which not only smooths the label distribution and reduces the risk of overfitting, but also effectively handles those difficult-to-classify samples, thus improving the overall classification accuracy. Experiments on three public datasets fully validate the superior performance of RFSS-NAS. [ABSTRACT FROM AUTHOR]

Atmospheric turbulence is a key factor contributing to data distortion in mid-to-long-range target observation tasks. Neural networks have become a powerful tool for dealing with such problems due to their strong ability to fit nonlinearities in the spatial domain. However, the degradation in data is not confined solely to the spatial domain but is also present in the frequency domain. In recent years, the academic community has come to recognize the significance of frequency domain information within neural networks. There remains a gap in research on how to combine dual-domain information to reconstruct high-quality images in the field of blind turbulence image restoration. Drawing upon the close association between spatial and frequency domain degradation information, we introduce a novel neural network architecture, termed Dual-Domain Removal Turbulence Network (DDRTNet), designed to improve the quality of reconstructed images. DDRTNet incorporates multiscale spatial and frequency domain attention mechanisms, combined with a dual-domain collaborative learning strategy, effectively integrating global and local information to achieve efficient restoration of atmospheric turbulence-degraded images. Experimental findings demonstrate significant advantages in performance for DDRTNet compared to existing methods, validating its effectiveness in the task of blind turbulence image restoration. [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]

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]

In this paper, based on high-resolution satellite images near an ice bridge in the Canadian Basin, we extracted floe size parameters and analyzed the temporal and spatial variations in the parameters through image processing techniques. The floe area shows a decreasing trend over time, while the perimeter and mean clamped diameter (MCD) exhibit no obvious pattern of change. In addition, the roundness of floes, reflected by shape parameters, generally decreases initially and then increases, and the average roundness of small floes is smaller than that of large floes. To correct the deviations from power law behaviour when assessing the floe size distribution (FSD) with the traditional power law function, the upper-truncated power law distribution function and the Weibull function are selected. The four parameters of the two functions are important parameters for describing the floe size distribution, and L r and L 0 are roughly equal to the maximum calliper diameter and the average calliper diameter of the floes in the region. D in the upper-truncated power law distribution function represents the fractal dimension of the floes, and r in the Weibull function represents the shape parameter of the floes, both of which increase and then decrease with time. In this paper, we investigate the response of the rate of change in the FSD parameter to the differences in the monthly average temperature and find that D , r and air temperature are positively correlated, which verifies the influence of air temperature on the floe size distribution. [ABSTRACT FROM AUTHOR]

To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness-consistency recovery method is proposed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from images. After image-level processing, a combined underwater acousto–optic feature-association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for its performance against the OKVIS framework. The system achieves a better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments. [ABSTRACT FROM AUTHOR]

Segmenting clouds and their shadows is a critical challenge in remote sensing image processing. The shape, texture, lighting conditions, and background of clouds and their shadows impact the effectiveness of cloud detection. Currently, architectures that maintain high resolution throughout the entire information-extraction process are rapidly emerging. This parallel architecture, combining high and low resolutions, produces detailed high-resolution representations, enhancing segmentation prediction accuracy. This paper continues the parallel architecture of high and low resolution. When handling high- and low-resolution images, this paper employs a hybrid approach combining the Transformer and CNN models. This method facilitates interaction between the two models, enabling the extraction of both semantic and spatial details from the images. To address the challenge of inadequate fusion and significant information loss between high- and low-resolution images, this paper introduces a method based on ASMA (Axial Sharing Mixed Attention). This approach establishes pixel-level dependencies between high-resolution and low-resolution images, aiming to enhance the efficiency of image fusion. In addition, to enhance the effective focus on critical information in remote sensing images, the AGM (Attention Guide Module) is introduced, to integrate attention elements from original features into ASMA, to alleviate the problem of insufficient channel modeling of the self-attention mechanism. Our experimental results on the Cloud and Cloud Shadow dataset, the SPARCS dataset, and the CSWV dataset demonstrate the effectiveness of our method, surpassing the state-of-the-art techniques for cloud and cloud shadow segmentation. [ABSTRACT FROM AUTHOR]

A horizontal towed linear coherent hydrophone array is often employed to estimate the spatial intensity distribution of incident plane waves scattered from the geological and biological features in an ocean acoustic waveguide using conventional beamforming. However, due to the physical limitations of the array aperture, the spatial resolution after conventional beamforming is often limited by the fat main lobe and the high sidelobes. Here, we propose a method originated from computer vision deblurring based on deep learning to enhance the spatial resolution of beamformed images. The effect of image blurring after conventional beamforming can be considered a convolution of beam pattern, which acts as a point spread function (PSF), and the original spatial intensity distributions of incident plane waves. A modified U-Net-like network is trained on a simulated dataset. The instantaneous acoustic complex amplitude is assumed following circular complex Gaussian random (CCGR) statistics. Both synthetic data and experimental data collected from the South China Sea Experiment in 2021 are used to illustrate the effectiveness of this approach, showing a maximum 700% reduction in a 3 dB width over conventional beamforming. A lower normalized mean square error (NMSE) is provided compared with other deconvolution-based algorithms, such as the Richardson–Lucy algorithm and the approximate likelihood model-based deconvolution algorithm. The method is applicable in various acoustic imaging applications that employ linear coherent hydrophone arrays with one-dimensional conventional beamforming, such as ocean acoustic waveguide remote sensing (OAWRS). [ABSTRACT FROM AUTHOR]

Sentinel-2 satellites are one of the major instruments in remote sensing (RS) technology that has revolutionized Earth observation research, as its main goal is to offer high-resolution satellite data for dynamic monitoring of Earth's surface and climate change detection amongst others. However, visual observation of Sentinel-2 satellite data has revealed that most images obtained during the winter season contain snow noise, posing a major challenge and impediment to satellite RS analysis of land surface. This singular effect hampers satellite signals from capturing important surface features within the geographical area of interest. Consequently, it leads to information loss, image processing problems due to contamination, and masking effects, all of which can reduce the accuracy of image analysis. In this study, we developed a snow-cover removal (SCR) model based on the Cycle-Consistent Adversarial Networks (CycleGANs) architecture. Data augmentation procedures were carried out to salvage the effect of the limited availability of Sentinel-2 image data. Sentinel-2 satellite images were used for model training and the development of a novel SCR model. The SCR model captures snow and other prominent features in the Sentinel-2 satellite image and then generates a new snow-free synthetic optical image that shares the same characteristics as the source satellite image. The snow-free synthetic images generated are evaluated to quantify their visual and semantic similarity with original snow-free Sentinel-2 satellite images by using different image qualitative metrics (IQMs) such as Structural Similarity Index Measure (SSIM), Universal image quality index (Q), and peak signal-to-noise ratio (PSNR). The estimated metric data shows that Q delivers more metric values, nearly 95%, than SSIM and PRSN. The methodology presented in this study could be beneficial for RS research in DL model development for environmental mapping and time series modeling. The results also confirm the DL technique's applicability in RS studies. [ABSTRACT FROM AUTHOR]

In remote sensing image processing, the segmentation of clouds and their shadows is a fundamental and vital task. For cloud images, traditional deep learning methods often have weak generalization capabilities and are prone to interference from ground objects and noise, which not only results in poor boundary segmentation but also causes false and missed detections of small targets. To address these issues, we proposed a multi-branch attention fusion network (MAFNet). In the encoder section, the dual branches of ResNet50 and the Swin transformer extract features together. A multi-branch attention fusion module (MAFM) uses positional encoding to add position information. Additionally, multi-branch aggregation attention (MAA) in the MAFM fully fuses the same level of deep features extracted by ResNet50 and the Swin transformer, which enhances the boundary segmentation ability and small target detection capability. To address the challenge of detecting small cloud and shadow targets, an information deep aggregation module (IDAM) was introduced to perform multi-scale deep feature aggregation, which supplements high semantic information, improving small target detection. For the problem of rough segmentation boundaries, a recovery guided module (RGM) was designed in the decoder section, which enables the model to effectively allocate attention to complex boundary information, enhancing the network's focus on boundary information. Experimental results on the Cloud and Cloud Shadow dataset, HRC-WHU dataset, and SPARCS dataset indicate that MAFNet surpasses existing advanced semantic segmentation techniques. [ABSTRACT FROM AUTHOR]

Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (OBARPs) are often comparable to those of the pixels surrounding them (SBARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of OBARPs and SBARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments. [ABSTRACT FROM AUTHOR]

Limited resources for edge computing platforms in airborne and spaceborne imaging payloads prevent using complex image processing models. Model pruning can eliminate redundant parameters and reduce the computational load, enhancing processing efficiency on edge computing platforms. Current challenges in model pruning for remote-sensing object detection include the risk of losing target features, particularly during sparse training and pruning, and difficulties in maintaining channel correspondence for residual structures, often resulting in retaining redundant features that compromise the balance between model size and accuracy. To address these challenges, we propose the L1 reweighted regularization (L1RR) pruning method. Leveraging dynamic and self-adaptive sparse modules, we optimize L1 sparsity regularization, preserving the model's target feature information using a feature attention loss mechanism to determine appropriate pruning ratios. Additionally, we propose a residual reconstruction procedure, which removes redundant feature channels from residual structures while maintaining the residual inference structure through output channel recombination and input channel recombination, achieving a balance between model size and accuracy. Validation on two remote-sensing datasets demonstrates significant reductions in parameters and floating point operations (FLOPs) of 77.54% and 65%, respectively, and a 48.5% increase in the inference speed on the Jetson TX2 platform. This framework optimally maintains target features and effectively distinguishes feature channel importance compared to other methods, significantly enhancing feature channel robustness for difficult targets and expanding pruning applicability to less difficult targets. [ABSTRACT FROM AUTHOR]

The real-time tracking of moving objects has extensive applications in various domains. Existing tracking methods typically utilize video image processing, but their performance is limited due to the high information throughput and computational requirements associated with processing continuous images. Additionally, imaging in certain spectral bands can be costly. This paper proposes a non-imaging real-time three-dimensional tracking technique for distant moving targets using single-pixel LiDAR. This novel approach involves compressing scene information from three-dimensional to one-dimensional space using spatial encoding modulation and then obtaining this information through single-pixel detection. A LiDAR system is constructed based on this method, where the peak position of the detected full-path one-dimensional echo signal is used to obtain the target distance, while the peak intensity is used to obtain the azimuth and pitch information of the moving target. The entire process requires minimal data collection and a low computational load, making it feasible for the real-time three-dimensional tracking of single or multiple moving targets. Outdoor experiments confirmed the efficacy of the proposed technology, achieving a distance accuracy of 0.45 m and an azimuth and pitch angle accuracy of approximately 0.03° in localizing and tracking a flying target at a distance of 3 km. [ABSTRACT FROM AUTHOR]

Segmenting small infrared targets presents a significant challenge for traditional image processing architectures due to the inherent lack of texture, minimal shape information, and their sparse pixel representation within images. The conventional UNet architecture, while proficient in general segmentation tasks, inadequately addresses the nuances of small infrared target segmentation due to its reliance on downsampling operations, such as pooling, which often results in the loss of critical target information. This paper introduces the Channel Spatial Attention Nested UNet (CSAN-UNet), an innovative architecture designed specifically to enhance the detection and segmentation of small infrared targets. Central to CSAN-UNet's design is the Cascaded Channel and Spatial Convolutional Attention Module (CSCAM), a novel component that adaptively enhances multi-level features and mitigates the loss of target information attributable to downsampling processes. Additionally, the Channel-priority and Spatial Attention Cascade Module (CPSAM) represents another pivotal advancement within CSAN-UNet, prioritizing channel-level adjustments alongside spatial attention mechanisms to efficiently extract deep semantic information pertinent to small infrared targets. Empirical validation conducted on two public datasets confirms that CSAN-UNet surpasses existing state-of-the-art algorithms in segmentation performance, while simultaneously reducing computational overhead. [ABSTRACT FROM AUTHOR]

Cloud detection technology is crucial in remote sensing image processing. While cloud detection is a mature research field, challenges persist in detecting clouds on reflective surfaces like ice, snow, and sand. Particularly, the detection of cloud shadows remains a significant area of concern within cloud detection technology. To address the above problems, a convolutional self-attention mechanism feature fusion network model based on a U-shaped structure is proposed. The model employs an encoder–decoder structure based on UNet. The encoder performs down-sampling to extract deep features, while the decoder uses up-sampling to reconstruct the feature map. To capture the key features of the image, Channel Spatial Attention Module (CSAM) is introduced in this work. This module incorporates an attention mechanism for adaptive field-of-view adjustments. In the up-sampling process, different channels are selected to obtain rich information. Contextual information is integrated to improve the extraction of edge details. Feature fusion at the same layer between up-sampling and down-sampling is carried out. The Feature Fusion Module (FFM) facilitates the positional distribution of the image on a pixel-by-pixel basis. A clear boundary is distinguished using an innovative loss function. Finally, the experimental results on the dataset GF1_WHU show that the segmentation results of this method are better than the existing methods. Hence, our model is of great significance for practical cloud shadow segmentation. [ABSTRACT FROM AUTHOR]

The effective segmentation of clouds and cloud shadows is crucial for surface feature extraction, climate monitoring, and atmospheric correction, but it remains a critical challenge in remote sensing image processing. Cloud features are intricate, with varied distributions and unclear boundaries, making accurate extraction difficult, with only a few networks addressing this challenge. To tackle these issues, we introduce a multi-scale receptive field aggregation network (MRFA-Net). The MRFA-Net comprises an MRFA-Encoder and MRFA-Decoder. Within the encoder, the net includes the asymmetric feature extractor module (AFEM) and multi-scale attention, which capture diverse local features and enhance contextual semantic understanding, respectively. The MRFA-Decoder includes the multi-path decoder module (MDM) for blending features and the global feature refinement module (GFRM) for optimizing information via learnable matrix decomposition. Experimental results demonstrate that our model excelled in generalization and segmentation performance when addressing various complex backgrounds and different category detections, exhibiting advantages in terms of parameter efficiency and computational complexity, with the MRFA-Net achieving a mean intersection over union (MIoU) of 94.12% on our custom Cloud and Shadow dataset, and 87.54% on the open-source HRC_WHU dataset, outperforming other models by at least 0.53% and 0.62%. The proposed model demonstrates applicability in practical scenarios where features are difficult to distinguish. [ABSTRACT FROM AUTHOR]

Remote sensing image dehazing is a well-known remote sensing image processing task focused on restoring clean images from hazy images. The Transformer network, based on the self-attention mechanism, has demonstrated remarkable advantages in various image restoration tasks, due to its capacity to capture long-range dependencies within images. However, it is weak at modeling local context. Conversely, convolutional neural networks (CNNs) are adept at capturing local contextual information. Local contextual information could provide more details, while long-range dependencies capture global structure information. The combination of long-range dependencies and local context modeling is beneficial for remote sensing image dehazing. Therefore, in this paper, we propose a CNN-based adaptive local context enrichment module (ALCEM) to extract contextual information within local regions. Subsequently, we integrate our proposed ALCEM into the multi-head self-attention and feed-forward network of the Transformer, constructing a novel locally enhanced attention (LEA) and a local continuous-enhancement feed-forward network (LCFN). The LEA utilizes the ALCEM to inject local context information that is complementary to the long-range relationship modeled by multi-head self-attention, which is beneficial to removing haze and restoring details. The LCFN extracts multi-scale spatial information and selectively fuses them by the the ALCEM, which supplements more informative information compared with existing regular feed-forward networks with only position-specific information flow. Powered by the LEA and LCFN, a novel Transformer-based dehazing network termed LCEFormer is proposed to restore clear images from hazy remote sensing images, which combines the advantages of CNN and Transformer. Experiments conducted on three distinct datasets, namely DHID, ERICE, and RSID, demonstrate that our proposed LCEFormer achieves the state-of-the-art performance in hazy scenes. Specifically, our LCEFormer outperforms DCIL by 0.78 dB and 0.018 for PSNR and SSIM on the DHID dataset. [ABSTRACT FROM AUTHOR]

Delineating and modelling building roof plane structures is an active research direction in urban-related studies, as understanding roof structure provides essential information for generating highly detailed 3D building models. Traditional deep-learning models have been the main focus of most recent research endeavors aiming to extract pixel-based building roof plane areas from remote-sensing imagery. However, significant challenges arise, such as delineating complex roof boundaries and invisible boundaries. Additionally, challenges during the post-processing phase, where pixel-based building roof plane maps are vectorized, often result in polygons with irregular shapes. In order to address this issue, this study explores a state-of-the-art method for planar graph reconstruction applied to building roof plane extraction. We propose a framework for reconstructing regularized building roof plane structures using aerial imagery and cadastral information. Our framework employs a holistic edge classification architecture based on an attention-based neural network to detect corners and edges between them from aerial imagery. Our experiments focused on three distinct study areas characterized by different roof structure topologies: the Stadsveld–'t Zwering neighborhood and Oude Markt area, located in Enschede, The Netherlands, and the Lozenets district in Sofia, Bulgaria. The outcomes of our experiments revealed that a model trained with a combined dataset of two different study areas demonstrated a superior performance, capable of delineating edges obscured by shadows or canopy. Our experiment in the Oude Markt area resulted in building roof plane delineation with an F-score value of 0.43 when the model trained on the combined dataset was used. In comparison, the model trained only on the Stadsveld–'t Zwering dataset achieved an F-score value of 0.37, and the model trained only on the Lozenets dataset achieved an F-score value of 0.32. The results from the developed approach are promising and can be used for 3D city modelling in different urban settings. [ABSTRACT FROM AUTHOR]

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

Recently, with the remarkable advancements of deep learning in the field of image processing, convolutional neural networks (CNNs) have garnered widespread attention from researchers in the domain of hyperspectral image (HSI) classification. Moreover, due to the high performance demonstrated by the transformer architecture in classification tasks, there has been a proliferation of neural networks combining CNNs and transformers for HSI classification. However, the majority of the current methods focus on extracting spatial–spectral features from the HSI data of a single size for a pixel, overlooking the rich multi-scale feature information inherent to the data. To address this problem, we designed a novel transformer network with a CNN-enhanced cross-attention (TNCCA) mechanism for HSI classification. It is a dual-branch network that utilizes different scales of HSI input data to extract shallow spatial–spectral features using a multi-scale 3D and 2D hybrid convolutional neural network. After converting the feature maps into tokens, a series of 2D convolutions and dilated convolutions are employed to generate two sets of Q (queries), K (keys), and V (values) at different scales in a cross-attention module. This transformer with CNN-enhanced cross-attention explores multi-scale CNN-enhanced features and fuses them from both branches. Experimental evaluations conducted on three widely used hyperspectral image (HSI) datasets, under the constraint of limited sample size, demonstrate excellent classification performance of the proposed network. [ABSTRACT FROM AUTHOR]

Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method's effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation. [ABSTRACT FROM AUTHOR]

Semantic segmentation of remote sensing (RS) images is a pivotal branch in the realm of RS image processing, which plays a significant role in urban planning, building extraction, vegetation extraction, etc. With the continuous advancement of remote sensing technology, the spatial resolution of remote sensing images is progressively improving. This escalation in resolution gives rise to challenges like imbalanced class distributions among ground objects in RS images, the significant variations of ground object scales, as well as the presence of redundant information and noise interference. In this paper, we propose a multi-scale context extraction network, ASPP+-LANet, based on the LANet for semantic segmentation of high-resolution RS images. Firstly, we design an ASPP+ module, expanding upon the ASPP module by incorporating an additional feature extraction channel, redesigning the dilation rates, and introducing the Coordinate Attention (CA) mechanism so that it can effectively improve the segmentation performance of ground object targets at different scales. Secondly, we introduce the Funnel ReLU (FReLU) activation function for enhancing the segmentation effect of slender ground object targets and refining the segmentation edges. The experimental results show that our network model demonstrates superior segmentation performance on both Potsdam and Vaihingen datasets, outperforming other state-of-the-art (SOTA) methods. [ABSTRACT FROM AUTHOR]

The creation of accurate elevation models (topography) from stereo images are critical for a large variety of geospatial activities, including the production of digital orthomosaics, change detection, landing site analysis, geologic mapping, rover traverse planning, and spectral analysis. The United Stated Geological Survey, Astrogeology Science Center, continues to transition the supported planetary sensor models to the Community Sensor Model (CSM) standard. This paper describes the current state of use for this photogrammetric standard, supported sensor model types, and qualitatively compares derived topography between SOCET SET and SOCET GXP (®BAE Systems) using HiRISE stereo images of Mars. Our transition to the CSM standard will ensure an uninterrupted capability to make these valuable products for Mars and many other extraterrestrial planets and moons. [ABSTRACT FROM AUTHOR]

Change detection in remote sensing enables identifying alterations in surface characteristics over time, underpinning diverse applications. However, conventional pixel-based algorithms encounter constraints in terms of accuracy when applied to medium- and high-resolution remote sensing images. Although object-oriented methods offer a step forward, they frequently grapple with missing small objects or handling complex features effectively. To bridge these gaps, this paper proposes an unsupervised object-oriented change detection approach empowered by hierarchical multi-scale segmentation for generating binary ecosystem change maps. This approach meticulously segments images into optimal sizes and leverages multidimensional features to adapt the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) algorithm for GaoFen WFV data. We rigorously evaluated its performance in the Yellow River Source Region, a critical ecosystem conservation zone. The results unveil three key strengths: (1) the approach achieved excellent object-level change detection results, making it particularly suited for identifying changes in subtle features; (2) while simply increasing object features did not lead to a linear accuracy gain, optimized feature space construction effectively mitigated dimensionality issues; and (3) the scalability of our approach is underscored by its success in mapping the entire Yellow River Source Region, achieving an overall accuracy of 90.09% and F-score of 0.8844. Furthermore, our analysis reveals that from 2015 to 2022, changed ecosystems comprised approximately 1.42% of the total area, providing valuable insights into regional ecosystem dynamics. [ABSTRACT FROM AUTHOR]

Tobacco is a critical cash crop in China, so its growing status has received more and more attention. How to acquire accurate plant area, row spacing, and plant spacing at the same time have been key points for its grow status monitoring and yield prediction. However, accurately detecting small and densely arranged tobacco plants during the rosette stage poses a significant challenge. In Sichuan Province, the contours of scattered tobacco fields with different shapes are not well-extracted. Additionally, there is a lack of simultaneous methods for extracting crucial tobacco planting information, including area, row spacing, and plant spacing. In view of the above scientific problems, we proposed a method to extract the planting information of tobacco at the rosette stage with Unmanned Aerial Vehicle (UAV) remote sensing images. A detection model, YOLOv8s-EFF, was constructed for the small and weak tobacco in the rosette stage. We proposed an extraction algorithm for tobacco field area based on extended contours for different-shaped fields. Meanwhile, a planting distance extraction algorithm based on tobacco coordinates was presented. Further, four experimental areas were selected in Sichuan Province, and image processing and sample label production were carried out. Four isolated tobacco fields with different shapes in four experimental areas were used to preliminarily verify the effectiveness of the model and algorithm proposed. The results show that the precision ranges of tobacco field area, row spacing, and plant spacing were 96.51~99.04%, 90.08~99.74%, and 94.69~99.15%, respectively. And another two experimental areas, Jiange County, Guangyuan, and Dazhai County, Gulin County, and Luzhou, were selected to evaluate the accuracy of the method proposed in the research in practical application. The results indicate that the average accuracy of tobacco field area, row spacing, and plant spacing extracted by this method reached 97.99%, 97.98%, and 98.31%, respectively, which proved the extraction method of plant information is valuable. [ABSTRACT FROM AUTHOR]

Hyperspectral classification is a task of significant importance in the field of remote sensing image processing, with attaining high precision and rapid classification increasingly becoming a research focus. The classification accuracy depends on the degree of raw HSI feature extraction, and the use of endless classification methods has led to an increase in computational complexity. To achieve high accuracy and fast classification, this study analyzes the inherent features of HSI and proposes a novel spectral–spatial feature extraction method called window shape adaptive singular spectrum analysis (WSA-SSA) to reduce the computational complexity of feature extraction. This method combines similar pixels in the neighborhood to reconstruct every pixel in the window, and the main steps are as follows: rearranging the spectral vectors in the irregularly shaped region, constructing an extended trajectory matrix, and extracting the local spatial and spectral information while removing the noise. The results indicate that, given the small sample sizes in the Indian Pines dataset, the Pavia University dataset, and the Salinas dataset, the proposed algorithm achieves classification accuracies of 97.56%, 98.34%, and 99.77%, respectively. The classification speed is more than ten times better than that of other methods, and a classification time of only about 1–2 s is needed. [ABSTRACT FROM AUTHOR]

Owing to the significant application potential of unmanned aerial vehicles (UAVs) and infrared imaging technologies, researchers from different fields have conducted numerous experiments on aerial infrared image processing. To continuously detect small road objects 24 h/day, this study proposes an efficient Rep-style Gaussian–Wasserstein network (ERGW-net) for small road object detection in infrared aerial images. This method aims to resolve problems of small object size, low contrast, few object features, and occlusions. The ERGW-net adopts the advantages of ResNet, Inception net, and YOLOv8 networks to improve object detection efficiency and accuracy by improving the structure of the backbone, neck, and loss function. The ERGW-net was tested on a DroneVehicle dataset with a large sample size and the HIT-UAV dataset with a relatively small sample size. The results show that the detection accuracy of different road targets (e.g., pedestrians, cars, buses, and trucks) is greater than 80%, which is higher than the existing methods. [ABSTRACT FROM AUTHOR]

In the domain of synthetic aperture radar (SAR) image processing, a prevalent issue persists wherein research predominantly focuses on single-task learning, often neglecting the concurrent impact of speckle noise and low resolution on SAR images. Currently, there are two main processing strategies. The first strategy involves conducting speckle reduction and super-resolution processing step by step. The second strategy involves performing speckle reduction as an auxiliary step, with a focus on enhancing the primary task of super-resolution processing. However, both of these strategies exhibit clear deficiencies. Nevertheless, both tasks jointly focus on two key aspects, enhancing SAR quality and restoring details. The fusion of these tasks can effectively leverage their task correlation, leading to a significant improvement in processing effectiveness. Additionally, multi-temporal SAR images covering imaging information from different time periods exhibit high correlation, providing deep learning models with a more diverse feature expression space, greatly enhancing the model's ability to address complex issues. Therefore, this study proposes a deep learning network for integrated speckle reduction and super-resolution in multi-temporal SAR (ISSMSAR). The network aims to reduce speckle in multi-temporal SAR while significantly improving the image resolution. Specifically, it consists of two subnetworks, each taking the SAR image at time 1 and the SAR image at time 2 as inputs. Each subnetwork includes a primary feature extraction block (PFE), a high-level feature extraction block (HFE), a multi-temporal feature fusion block (FFB), and an image reconstruction block (REC). Following experiments on diverse data sources, the results demonstrate that ISSMSAR surpasses speckle reduction and super-resolution methods based on a single task in terms of both subjective perception and objective evaluation metrics regarding the quality of image restoration. [ABSTRACT FROM AUTHOR]

Built heritage materials assessment is an important task for planning and managing future conservation works. The uniqueness of each historical building makes reconnaissance operations more complex and specific for every single building. In the past, visual inspection and invasive techniques were widely used to investigate surface materials. Non-destructive techniques (NDTs) such as multi-band photogrammetry and remote sensing can help to assess the buildings without any contact with the investigated objects, restricting the disruptive tests on limited areas and reducing the testing time and costs of the surveys. This paper presents the results obtained using multi-band images acquired with a low-cost imaging solution after interchanging several filters, and the application of the principal components analysis (PCA) to recognize different materials of a significant historical monument. The Serranos Towers, built between 1392 and 1398, suffered several interventions in the past that affected their state of conservation with the replacement of different materials. The results of the study show the usefulness of applying PCA to distinguish different surface materials, often similar to the original ones, in a fast and efficient way to investigate and analyze our heritage legacy.

This study is structured to address the problem of mapping the bottom of shallow and ultra-shallow inland water bodies using high-resolution satellite imagery. These environments, with their diverse distribution of optically relevant components, pose a challenge to traditional mapping methods. The study was conducted on several research issues, each focusing on a specific aspect of the SDB, related to the selection of spectral bands and regression models, regression models creation, evaluation of the influence of the number and spatial distribution of reference soundings, and assessment of the quality of the bathymetric surface, with a focus on microtopography. The study utilized basic empirical techniques, incorporating high-precision reference data acquired via an unmanned surface vessel (USV) integrated with a single-beam echosounder (SBES), and Global Navigation Satellite System (GNSS) receiver measurements. The performed investigation allowed the optimization of a methodology for bathymetry acquisition of such areas by identifying the impact of individual processing components. The first results indicated the usefulness of the proposed approach, which can be confirmed by the values of the obtained RMS errors of elaborated bathymetric surfaces in the range of up to several centimeters in some study cases. The work also points to the problematic nature of this type of study, which can contribute to further research into the application of remote sensing techniques for bathymetry, especially during acquisition in optically complex waters.

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.

Atmospheric turbulence is a key factor contributing to data distortion in mid-to-long-range target observation tasks. Neural networks have become a powerful tool for dealing with such problems due to their strong ability to fit nonlinearities in the spatial domain. However, the degradation in data is not confined solely to the spatial domain but is also present in the frequency domain. In recent years, the academic community has come to recognize the significance of frequency domain information within neural networks. There remains a gap in research on how to combine dual-domain information to reconstruct high-quality images in the field of blind turbulence image restoration. Drawing upon the close association between spatial and frequency domain degradation information, we introduce a novel neural network architecture, termed Dual-Domain Removal Turbulence Network (DDRTNet), designed to improve the quality of reconstructed images. DDRTNet incorporates multiscale spatial and frequency domain attention mechanisms, combined with a dual-domain collaborative learning strategy, effectively integrating global and local information to achieve efficient restoration of atmospheric turbulence-degraded images. Experimental findings demonstrate significant advantages in performance for DDRTNet compared to existing methods, validating its effectiveness in the task of blind turbulence image restoration.

In this paper, based on high-resolution satellite images near an ice bridge in the Canadian Basin, we extracted floe size parameters and analyzed the temporal and spatial variations in the parameters through image processing techniques. The floe area shows a decreasing trend over time, while the perimeter and mean clamped diameter (MCD) exhibit no obvious pattern of change. In addition, the roundness of floes, reflected by shape parameters, generally decreases initially and then increases, and the average roundness of small floes is smaller than that of large floes. To correct the deviations from power law behaviour when assessing the floe size distribution (FSD) with the traditional power law function, the upper-truncated power law distribution function and the Weibull function are selected. The four parameters of the two functions are important parameters for describing the floe size distribution, and Lr and L0 are roughly equal to the maximum calliper diameter and the average calliper diameter of the floes in the region. D in the upper-truncated power law distribution function represents the fractal dimension of the floes, and r in the Weibull function represents the shape parameter of the floes, both of which increase and then decrease with time. In this paper, we investigate the response of the rate of change in the FSD parameter to the differences in the monthly average temperature and find that D, r and air temperature are positively correlated, which verifies the influence of air temperature on the floe size distribution.

A horizontal towed linear coherent hydrophone array is often employed to estimate the spatial intensity distribution of incident plane waves scattered from the geological and biological features in an ocean acoustic waveguide using conventional beamforming. However, due to the physical limitations of the array aperture, the spatial resolution after conventional beamforming is often limited by the fat main lobe and the high sidelobes. Here, we propose a method originated from computer vision deblurring based on deep learning to enhance the spatial resolution of beamformed images. The effect of image blurring after conventional beamforming can be considered a convolution of beam pattern, which acts as a point spread function (PSF), and the original spatial intensity distributions of incident plane waves. A modified U-Net-like network is trained on a simulated dataset. The instantaneous acoustic complex amplitude is assumed following circular complex Gaussian random (CCGR) statistics. Both synthetic data and experimental data collected from the South China Sea Experiment in 2021 are used to illustrate the effectiveness of this approach, showing a maximum 700% reduction in a 3 dB width over conventional beamforming. A lower normalized mean square error (NMSE) is provided compared with other deconvolution-based algorithms, such as the Richardson–Lucy algorithm and the approximate likelihood model-based deconvolution algorithm. The method is applicable in various acoustic imaging applications that employ linear coherent hydrophone arrays with one-dimensional conventional beamforming, such as ocean acoustic waveguide remote sensing (OAWRS).

In recent years, convolutional neural networks (CNNs) have gained widespread adoption in remote sensing image processing. Deploying CNN-based algorithms on satellite edge devices can alleviate the strain on data downlinks. However, CNN algorithms present challenges due to their large parameter count and high computational requirements, which conflict with the satellite platforms' low power consumption and high real-time requirements. Moreover, remote sensing image processing tasks are diverse, requiring the platform to accommodate various network structures. To address these issues, this paper proposes an algorithm–hardware co-optimization and deployment method for FPGA-based CNN remote sensing image processing. Firstly, a series of hardware-centric model optimization techniques are proposed, including operator fusion and depth-first mapping technology, to minimize the resource overhead of CNN models. Furthermore, a versatile hardware accelerator is proposed to accelerate a wide range of commonly used CNN models after optimization. The accelerator architecture mainly consists of a parallel configurable network processing unit and a multi-level storage structure, enabling the processing of optimized networks with high throughput and low power consumption. To verify the superiority of our method, the introduced accelerator was deployed on an AMD-Xilinx VC709 evaluation board, on which the improved YOLOv2, VGG-16, and ResNet-34 networks were deployed. Experiments show that the power consumption of the accelerator is 14.97 W, and the throughput of the three networks reaches 386.74 giga operations per second (GOPS), 344.44 GOPS, and 182.34 GOPS, respectively. Comparison with related work demonstrates that the co-optimization and deployment method can accelerate remote sensing image processing CNN models and is suitable for applications in satellite edge devices. [ABSTRACT FROM AUTHOR]

Spectral unmixing poses a significant challenge within hyperspectral image processing, traditionally addressed by supervised convolutional neural network (CNN)-based approaches employing patch-to-pixel (pixel-wise) methods. However, such pixel-wise methodologies often necessitate image splitting into overlapping patches, resulting in redundant computations and potential information leakage between training and test samples, consequently yielding overoptimistic outcomes. To overcome these challenges, this paper introduces a novel patch-to-patch (patch-wise) framework with nonoverlapping splitting, mitigating the need for repetitive calculations and preventing information leakage. The proposed framework incorporates a novel neural network structure inspired by the fully convolutional network (FCN), tailored for patch-wise unmixing. A highly efficient band reduction layer is incorporated to reduce the spectral dimension, and a specialized abundance constraint module is crafted to enforce both the Abundance Nonnegativity Constraint and the Abundance Sum-to-One Constraint for unmixing tasks. Furthermore, to enhance the performance of abundance estimation, a spatial–spectral attention module is introduced to activate the most informative spatial areas and feature maps. Extensive quantitative experiments and visual assessments conducted on two synthetic datasets and three real datasets substantiate the superior performance of the proposed algorithm. Significantly, the method achieves an impressive R M S E loss of 0.007, which is at least 4.5 times lower than that of other baselines on Urban hyperspectral images. This outcome demonstrates the effectiveness of our approach in addressing the challenges of spectral unmixing. [ABSTRACT FROM AUTHOR]

This study explores the innovative use of digital image processing (DIP) techniques, also named PhotoMonitoring, for analysing the triggering conditions of shallow landslides. The approach, based on the combination of optical and infrared thermographic imaging (IRT), was applied to a laboratory-scale slope, reproduced in a flume test apparatus. Three experiments were conducted to replicate rainfall-induced shallow landslides, applying change detection and digital image correlation analysis to both optical and thermal images. The method combines IRT's ability to measure ground surface temperature changes with DIP's capacity to track movement and displacement. Results showed the high reliability of the displacement time-series obtained through IRT-DIP with respect to the reference optical-DIP. The IRT-DIP technique also detects anomaly signals two minutes before landslide occurrence that can be regarded as a possible failure precursor. This study testifies to the potential of image analysis as a remote sensing technique, demonstrating the ability of DIP to capture the dynamics of shallow landslides, as well as the advantages of optical–IRT combinations to follow slope deformation processes during night-time. This approach, if properly adapted to real-scale scenarios, may contribute to a better understanding of landslide behaviour, improve landslide monitoring strategies, and promote more effective early warning systems (EWS). [ABSTRACT FROM AUTHOR]

Techniques and tools meant to aid fire management activities in the Cerrado, such as accurately determining the fuel load and composition spatially and temporally, are pretty scarce. The need to obtain fuel information for more efficient management in a considerably heterogeneous, biodiverse, and fire-dependent environment requires a constant search for improved remote sensing techniques for determining fuel characteristics. This study presents the following objectives: (1) to assess the use of data from Landsat 8 OLI images to estimate the fine surface fuel load of the Cerrado during the dry season by adjusting multiple linear regression equations, (2) to estimate the fuel load through random forest and k-nearest neighbor (k-NN) algorithms in comparison to regression analyses, and (3) to evaluate the importance of predictor variables from satellite images. Therefore, 64 sampling units were collected, and the pixel values associated with the field plots were extracted in a 3 × 3-pixel window surrounding the reference pixel. For multiple linear regression analyses, the R2 values ranged from 0.63 to 0.78, while the R2 values of the models fitted using the random forest algorithm ranged from 0.52 to 0.83 and the R2 values of those fitted using the k-NN algorithm ranged from 0.30 to 0.68. The estimates made through multiple linear regression analyses showed better results for the equations adjusted for the beginning of the dry season (May and June). Adopting the random forest algorithm resulted in improvements in the statistical metrics of evaluation of the fuel load estimates for the Cerrado grassland relative to multiple linear regression analyses. The variable fraction-soil (FS) exerted the most significant effect on surface fuel load estimates, followed by the vegetation indices NDII, GVMI, DER56, NBR, and MSI, all of which use near-infrared and short-wave infrared channels in their calculations. [ABSTRACT FROM AUTHOR]

Cloud detection is critical in remote sensing image processing, and convolutional neural networks (CNNs) have significantly advanced this field. However, traditional CNNs primarily focus on extracting local features, which can be challenging for cloud detection due to the variability in the size, shape, and boundaries of clouds. To address this limitation, we propose a hybrid Swin transformer–CNN cloud detection (STCCD) network that combines the strengths of both architectures. The STCCD network employs a novel dual-stream encoder that integrates Swin transformer and CNN blocks. Swin transformers can capture global context features more effectively than traditional CNNs, while CNNs excel at extracting local features. The two streams are fused via a fusion coupling module (FCM) to produce a richer representation of the input image. To further enhance the network's ability in extracting cloud features, we incorporate a feature fusion module based on the attention mechanism (FFMAM) and an aggregation multiscale feature module (AMSFM). The FFMAM selectively merges global and local features based on their importance, while the AMSFM aggregates feature maps from different spatial scales to obtain a more comprehensive representation of the cloud mask. We evaluated the STCCD network on three challenging cloud detection datasets (GF1-WHU, SPARCS, and AIR-CD), as well as the L8-Biome dataset to assess its generalization capability. The results show that the STCCD network outperformed other state-of-the-art methods on all datasets. Notably, the STCCD model, trained on only four bands (visible and near-infrared) of the GF1-WHU dataset, outperformed the official Landsat-8 Fmask algorithm in the L8-Biome dataset, which uses additional bands (shortwave infrared, cirrus, and thermal). [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the journal Remote Sensing. The issue focuses on hyperspectral remote sensing from spaceborne and low-altitude aerial/drone-based platforms. The editorial highlights the advancements in hyperspectral remote sensing imagery and the progress of novel image processing techniques. It also discusses the limitations and potential applications of spaceborne and aerial/drone-based hyperspectral sensors. The editorial provides a summary of the papers included in the special issue, which cover a range of topics such as wetland mapping, mineral exploration, water quality estimation, and land-use change analysis. The authors express their gratitude to the contributors and reviewers of the special issue and hope that the studies will provide valuable insights to remote sensing researchers. [Extracted from the article]

Operational applications for Synthetic Aperture Radar (SAR) are under development around the world, driven by the free-and-open access of SAR C-band observations that Sentinel-1 of Copernicus has provided since 2014. Radiometric Terrain Correction (RTC) data are key entry-level products for multiple applications ranging from ecosystem to hazard monitoring. Various open-source software packages exist to create RTC products from Single Look Complex (SLC) or Ground Range Detected (GRD) level SAR data, including the Interferometric SAR Computing Environment (ISCE), and the Sentinel-1 Toolbox from the European Space Agency (SNAP 8). Despite the growing availability of RTC software solutions, little work has been performed to identify differences between RTC products generated using different software packages. This work evaluates several Sentinel-1 RTC products and two other Sentinel-1 Analysis Ready Data (ARD) to address the following questions: (1) Which software provides the most accurate RTC product? and (2) how appropriate for analysis are other non-RTC products that are readily available? The RTCs are produced with GAMMA, ISCE-2, and SNAP 8. The other two ARD products evaluated consisted of an angular-based radiometric slope correction produced in Google Earth Engine (GEE) following Vollrath et al., and the Sentinel-1 GRD product. Products are evaluated across 10 sites in a single image approach for (1) radiometric calibration, (2) geometric corrections, and for (3) geolocation quality. In addition, time-series stacks over two sites representing varied terrain and ecosystems are evaluated. The GAMMA-derived RTC product implemented by the Alaska Satellite Facility (ASF) is used as a reference for some of the time-series metrics. The results provide direct guidance and recommendations about the quality of the RTC and ARD products obtained from open source methods. The results indicate that it is not recommended to use the GRD product with no radiometric or geometric corrections for any applications given low performance in multiple metrics. The radiometric calibration and geometric corrections have overall good performance for all open-source solutions, only the non-RTC products (Vollrath et al. and GRD) portray some significant variances in steep terrain. The geolocation assessment indicated that the GRD product has the most significant displacement errors, followed by SNAP 8 with Digital Elevation Model (DEM) matching, and ISCE-2. RTCs created without DEM-matching performed better for both GAMMA and SNAP 8. The time-series results indicate that SNAP 8 products align more closely to GAMMA products than other open-source software in terms of radiometric and geometric quality. This understanding of software performance for SAR image processing is key to designing the affordable and scalable solutions needed for the operational application of SAR Sentinel-1 data. [ABSTRACT FROM AUTHOR]

Mangrove forests in developed and developing countries are experiencing substantial transformations driven by natural and anthropogenic factors. This study focuses on the conterminous United States, including Florida, Texas, and Louisiana, where coastal development, urbanization, hydrological pattern alterations, global warming, sea level rise, and natural disasters such as hurricanes contribute to mangrove forest changes. Using time-series Landsat data and image-processing techniques in a cloud computing platform, we analyzed the dynamics of mangrove forests every five years from 1980 to 2020. Each thematic product was independently derived using a region of interest (ROI) suitable for local conditions. The analysis was performed using consistent data sources and a unified classification methodology. Our results revealed that the total mangrove area in the conterminous United States (CONUS) in 2020 was 266,179 ha. with 98.0% of the mangrove area in Florida, 0.6% in Louisiana, and 1.4% in Texas. Approximately 85% of the CONUS mangrove area was found between 24.5° and 26.0° latitude. Overall, mangrove forests in the CONUS increased by 13.5% from 1980 to 2020. However, the quinquennial variation in aerial coverage fluctuated substantially. The validation of 2020 using a statistical sample of reference data confirmed the high accuracy of 95%. Our results can aid policymakers and conservationists in developing targeted strategies for preserving the ecological and socio-economic value of mangrove forests in the conterminous United States. Additionally, all the datasets generated from this study have been released to the public. [ABSTRACT FROM AUTHOR]

Cloud and cloud shadow segmentation is one of the most critical challenges in remote sensing image processing. Because of susceptibility to factors such as disturbance from terrain features and noise, as well as a poor capacity to generalize, conventional deep learning networks, when directly used to cloud and cloud shade detection and division, have a tendency to lose fine features and spatial data, leading to coarse segmentation of cloud and cloud shadow borders, false detections, and omissions of targets. To address the aforementioned issues, a multi-scale strip feature attention network (MSFANet) is proposed. This approach uses Resnet18 as the backbone for obtaining semantic data at multiple levels. It incorporates a particular attention module that we name the deep-layer multi-scale pooling attention module (DMPA), aimed at extracting multi-scale contextual semantic data, deep channel feature information, and deep spatial feature information. Furthermore, a skip connection module named the boundary detail feature perception module (BDFP) is introduced to promote information interaction and fusion between adjacent layers of the backbone network. This module performs feature exploration on both the height and width dimensions of the characteristic pattern to enhance the recovery of boundary detail intelligence of the detection targets. Finally, during the decoding phase, a self-attention module named the cross-layer self-attention feature fusion module (CSFF) is employed to direct the aggregation of deeplayer semantic feature and shallow detail feature. This approach facilitates the extraction of feature information to the maximum extent while conducting image restoration. The experimental outcomes unequivocally prove the efficacy of our network in effectively addressing complex cloud-covered scenes, showcasing good performance across the cloud and cloud shadow datasets, the HRC_WHU dataset, and the SPARCS dataset. Our model outperforms existing methods in terms of segmentation accuracy, underscoring its paramount importance in the field of cloud recognition research. [ABSTRACT FROM AUTHOR]

The quality of remote sensing images has been greatly improved by the rapid improvement of unmanned aerial vehicles (UAVs), which has made it possible to detect small objects in the most complex scenes. Recently, learning-based object detection has been introduced and has gained popularity in remote sensing image processing. To improve the detection accuracy of small, weak objects in complex scenes, this work proposes a novel hybrid backbone composed of a convolutional neural network and an adaptive multi-scaled transformer, referred to as HAM-Transformer Net. HAM-Transformer Net firstly extracts the details of feature maps using convolutional local feature extraction blocks. Secondly, hierarchical information is extracted, using multi-scale location coding. Finally, an adaptive multi-scale transformer block is used to extract further features in different receptive fields and to fuse them adaptively. We implemented comparison experiments on a self-constructed dataset. The experiments proved that the method is a significant improvement over the state-of-the-art object detection algorithms. We also conducted a large number of comparative experiments in this work to demonstrate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

Arbitrarily oriented object detection is one of the most-popular research fields in remote sensing image processing. In this paper, we propose an approach to predict object angles indirectly, thereby avoiding issues related to angular periodicity and boundary discontinuity. Our method involves representing the long edge and angle of an object as a vector, which we then decompose into horizontal and vertical components. By predicting the two components of the vector, we can obtain the angle information of the object indirectly. To facilitate the transformation between angle-based representation and the proposed vector-decomposition-based representation, we introduced two novel techniques: angle-to-vector encode (ATVEncode) and vector-to-angle decode (VTADecode). These techniques not only improve the efficiency of data processing, but also accelerate the training process. Furthermore, we propose an adaptive coarse-to-fine positive–negative-sample-selection (AdaCFPS) method based on the vector-decomposition-based representation of the object. This method utilizes the Kullback–Leibler divergence loss as a matching degree to dynamically select the most-suitable positive samples. Finally, we modified the YOLOX model to transform it into an arbitrarily oriented object detector that aligns with our proposed vector-decomposition-based representation and positive–negative-sample-selection method. We refer to this redesigned model as the vector-decomposition-based object detector (VODet). In our experiments on the HRSC2016, DIOR-R, and DOTA datasets, VODet demonstrated notable advantages, including fewer parameters, faster processing speed, and higher precision. These results highlighted the significant potential of VODet in the context of arbitrarily oriented object detection. [ABSTRACT FROM AUTHOR]

In recent years, transformers have shown great potential in hyperspectral image processing and have also been gradually applied in hyperspectral target detection (HTD). Nonetheless, applying a typical transformer to HTD remains challenging. The heavy computation burden of the multi-head self-attention (MSA) in transformers limits its efficient HTD, while the limited ability to extract local spectral features can reduce the discrimination of the learned spectral features. To further explore the potential of transformers for HTD, for balance of representation ability and computational efficiency, we propose a dual-branch Fourier-mixing transformer network for hyperspectral target detection (DBFTTD). First, this work explores a dual-branch Fourier-mixing transformer network. The transformer-style network replaces the MSA sublayer in the transformer with a Fourier-mixing sublayer, which shows advantages in improving computational efficiency and learning valuable spectral information effectively for HTD. Second, this work proposes learnable filter ensembles in the Fourier domain that are inspired by ensemble learning to improve detection performance. Third, a simple but efficient dropout strategy is proposed for data augmentation. Sufficient and balanced training samples are constructed for training the dual-branch network, and training samples for balanced learning can further improve detection performance. Experiments on four data sets indicate that our proposed detector is superior to the state-of-the-art detectors. [ABSTRACT FROM AUTHOR]