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An automatic identification system (AIS) is often installed on merchant ships and fishing boats to prevent collisions and ensure safe navigation. The location information of ships transmitted from AIS equipment can help maritime traffic control prevent accidents. The southern coast of Korea comprises a complex coastline with numerous fishing boats and transit vessels. In particular, the Tongyeong and Geoje Islands include high-altitude mountains and islands, resulting in several radio-shaded areas where AIS signals cannot be received, owing to geographical effects. However, only a few studies have explored this region and performed practical experiments on the reception status of AIS locations in radio-shaded areas. In this study, we performed an experiment in the Geoje Island Sea on the southern coast to analyze the impact of high terrain on the reception rate and status of automatic identification devices. Two identical pieces of AIS equipment were installed to generate multiple radio waves, and the location data transmitted via different antennae were compared. The experimental analysis forms the basis for identifying the exact location of ships in the event of maritime accidents, facilitating rapid rescue. Moreover, the accuracy of the location transmitted by the AIS equipment can aid in detecting the cause of accidents. [ABSTRACT FROM AUTHOR]

Landslides are the most serious geological disaster in our country, causing economic losses. Because they go undetected, a large number of landslides that have caused disasters are not in the catalogue. At present, Interferometric Synthetic Aperture Radar (InSAR) has been widely used in the identification of landslides. However, it is time-consuming, inefficient, etc., to survey landslides throughout our large country. In the context of massive SAR data, this problem is more obvious. Therefore, based on the current technique of using differential interferogram phase gradient stacking to avoid phase unwrapping errors, a landslide phase gradient dataset has been constructed. To validate the dataset's effectiveness and applicability, deep learning methods were introduced, applying the dataset to four networks: U-Net, Attention-Unet, Bisenet v2, and Deeplab v3. The results indicate that the phase gradient dataset performs well across different models, with the Attention-Unet network demonstrating the best performance. Specifically, the precision, recall, and accuracy on the test dataset were 0.8771, 0.8712, and 0.9834, respectively, and the accuracy on the validation dataset was 0.8523. Finally, in this paper, the model is applied to landslide identification in Gansu Province, China, during 2022-2023, and a total of 1882 landslides are found. These landslides are mainly concentrated in the south of Gansu Province, where the terrain is relatively undulating. The results show that this method can quickly and accurately realize landslide automatic identification in a wide area and provide technical support for large-scale landslide disaster surveys. [ABSTRACT FROM AUTHOR]

Automatic modulation recognition (AMR) is widely employed in communication systems. However, under conditions of low signal-to-noise ratio (SNR), recent studies reveal limitations in achieving high AMR accuracy. In this work, we introduce a novel network architecture that leverages a transformer-inspired approach tailored for AMR, called Feature-Enhanced Transformer with skip-attention (FE-SKViT). This innovative design adeptly harnesses the advantages of translation variant convolution and the Transformer framework, handling intra-signal variance and small cross-signal variance to achieve enhanced recognition accuracy. Experimental results on RadioML2016.10a, RadioML2016.10b, and RML22 datasets demonstrate that the Feature-Enhanced Transformer with skip-attention (FE-SKViT) excels over other methods, particularly under low SNR conditions ranging from −4 to 6 dB.

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901.

The utilization of deep learning in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) has witnessed a recent surge owing to its remarkable feature extraction capabilities. Nonetheless, deep learning methodologies are often encumbered by inadequacies in labeled data and the protracted nature of training processes. To address these challenges and offer an alternative avenue for accurately extracting image features, this paper puts forth a novel and distinctive network dubbed the Capsule Broad Learning System Network for robust SAR ATR (CBLS-SARNET). This novel strategy is specifically tailored to cater to small-sample SAR ATR scenarios. On the one hand, we introduce a United Division Co-training (UDC) Framework as a feature filter, adeptly amalgamating CapsNet and the Broad Learning System (BLS) to enhance network efficiency and efficacy. On the other hand, we devise a Parameters Sharing (PS) network to facilitate secondary learning by sharing the weight and bias of BLS node layers, thereby augmenting the recognition capability of CBLS-SARNET. Experimental results unequivocally demonstrate that our proposed CBLS-SARNET outperforms other deep learning methods in terms of recognition accuracy and training time. Furthermore, experiments validate the generalization and robustness of our novel method under various conditions, including the addition of blur, Gaussian noise, noisy labels, and different depression angles. These findings underscore the superior generalization capabilities of CBLS-SARNET across diverse SAR ATR scenarios. [ABSTRACT FROM AUTHOR]

The ionospheric quasi-periodic wave is a type of typical and common electromagnetic wave phenomenon occurring in extremely low-frequency (ELF) and very low-frequency ranges (VLF). These emissions propagate in a distinct whistler-wave mode, with varying periodic modulations of the wave intensity over time scales from several seconds to a few minutes. We developed an automatic detection model for the QP waves in the ELF band recorded by the China Seismo-Electromagnetic Satellite. Based on the 827 QP wave events, which were collected through visual screening from the electromagnetic field observations, an automatic detection model based on the Transformer architecture was built. This model, comprising 34.27 million parameters, was trained and evaluated. It achieved mean average precision of 92.3% on the validation dataset, operating at a frame rate of 39.3 frames per second. Notably, after incorporating the proton cyclotron frequency constraint, the model displayed promising performance. Its lightweight design facilitates easy deployment on satellite equipment, significantly enhancing the feasibility of on-board detection.

An automatic identification system (AIS) is often installed on merchant ships and fishing boats to prevent collisions and ensure safe navigation. The location information of ships transmitted from AIS equipment can help maritime traffic control prevent accidents. The southern coast of Korea comprises a complex coastline with numerous fishing boats and transit vessels. In particular, the Tongyeong and Geoje Islands include high-altitude mountains and islands, resulting in several radio-shaded areas where AIS signals cannot be received, owing to geographical effects. However, only a few studies have explored this region and performed practical experiments on the reception status of AIS locations in radio-shaded areas. In this study, we performed an experiment in the Geoje Island Sea on the southern coast to analyze the impact of high terrain on the reception rate and status of automatic identification devices. Two identical pieces of AIS equipment were installed to generate multiple radio waves, and the location data transmitted via different antennae were compared. The experimental analysis forms the basis for identifying the exact location of ships in the event of maritime accidents, facilitating rapid rescue. Moreover, the accuracy of the location transmitted by the AIS equipment can aid in detecting the cause of accidents.

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method.

Slow-moving landslides often occur in areas of high relief, which are significantly affected by tropospheric delay. In general, tropospheric delay correction methods in the synthetic-aperture radar interferometry (InSAR) field can be broadly divided into those based on external auxiliary information and those based on traditional empirical models. External auxiliary information is hindered by the low spatial–temporal resolution. Traditional empirical models can be adaptable for the spatial heterogeneity of tropospheric delay, but are limited by preset window sizes and models. In this regard, this paper proposes an improved tropospheric delay correction method based on the multivariable move-window variation model (MMVM) to adaptively determine the window size and the empirical model. Considering topography and surface deformation, the MMVM uses multivariate variogram models with iterative weight to determine the window size and model, and uses the Levenberg–Marquardt (LM) algorithm to enhance convergence speed and robustness. The high-precision surface deformation is then derived. Combined with hotspot analysis (HSA), wide-area potential landslides can be automatically identified. The reservoir area of the Baihetan hydropower station in the lower reaches of the Jinsha River was selected as the study area, using 118 Sentinel-1A images to compare with four methods in three aspects: corrected interferograms, derived deformation rate, and stability of time-series deformation. In terms of mean standard deviation, the MMVM achieved the lowest value for the unwrapped phase in the non-deformed areas, representing a reduction of 56.4% compared to the original value. Finally, 32 landslides were identified, 16 of which posed a threat to nearby villages. The experimental results demonstrate the superiority of the proposed method and provide support to disaster investigation departments. [ABSTRACT FROM AUTHOR]

Automatic modulation recognition (AMR) is widely employed in communication systems. However, under conditions of low signal-to-noise ratio (SNR), recent studies reveal limitations in achieving high AMR accuracy. In this work, we introduce a novel network architecture that leverages a transformer-inspired approach tailored for AMR, called Feature-Enhanced Transformer with skip-attention (FE-SKViT). This innovative design adeptly harnesses the advantages of translation variant convolution and the Transformer framework, handling intra-signal variance and small cross-signal variance to achieve enhanced recognition accuracy. Experimental results on RadioML2016.10a, RadioML2016.10b, and RML22 datasets demonstrate that the Feature-Enhanced Transformer with skip-attention (FE-SKViT) excels over other methods, particularly under low SNR conditions ranging from −4 to 6 dB. [ABSTRACT FROM AUTHOR]

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901. [ABSTRACT FROM AUTHOR]

Crop recognition with high accuracy at a large scale is hampered by the spatial heterogeneity of crop growth characteristics under the complex influence of environmental conditions. With the aim to automatically realize large-scale crop classification with high accuracy, this study proposes an automatic crop classification strategy considering spatial heterogeneity (ACCSH) by combining the geographic detector technique, random forest average accuracy model, and random forest classification model. In ACCSH, spectral and textural indexes that can quantify crop growth characteristics and environmental variables with potential driving effects are first calculated. Next, an adaptive spatial heterogeneity mining method based on the geographic detector technique is proposed to mine spatial homogeneous zones adaptively with significant differentiation of crop growth characteristics. Subsequently, in view of the differences in crop growth characteristics and key classification indexes between spatial homogeneous zones, correlation analysis, and random forest average accuracy are combined to optimize classification indexes independently within each zone. Finally, random forest is used to classify the target crop in each spatial homogeneous zone separately. The proposed ACCSH is applied to automatically recognize crop types, specifically wheat and corn, in northern France. Results show that kappa coefficients of wheat and corn using ACCSH are 15% and 26% higher than those of classifications at the global scale, respectively. In addition, the index optimization strategy in ACCSH shows apparent superiority. Kappa coefficients of wheat and corn are 5–18% and 9–42% higher than those of classifications based on non-optimized indexes, respectively. In general, ACCSH can automatically realize crop classification with a high precision that suggests its reliability. [ABSTRACT FROM AUTHOR]

Instance segmentation is pivotal in remote sensing image (RSI) analysis, aiding in many downstream tasks. However, annotating images with pixel-wise annotations is time-consuming and laborious. Despite some progress in automatic annotation, the performance of existing methods still needs improvement due to the high precision requirements for pixel-level annotation and the complexity of RSIs. With the support of large-scale data, some foundational models have made significant progress in semantic understanding and generalization capabilities. In this paper, we delve deep into the potential of the foundational models in automatic annotation and propose a training-free automatic annotation method called DiffuPrompter, achieving pixel-level automatic annotation of RSIs. Extensive experimental results indicate that the proposed method can provide reliable pseudo-labels, significantly reducing the annotation costs of the segmentation task. Additionally, the cross-domain validation experiments confirm the powerful effectiveness of large-scale pseudo-data in improving model generalization performance.

This paper aims to analyze the North Brazil Current (NBC) rings during the initial 5 months of 2020 using surface currents derived from Automatic Identification System (AIS) data in comparison with altimetry-based Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO) current fields. The region of NBC rings is characterized by relatively high marine traffic, facilitating an accurate current estimation. Our investigation primarily focused on a brief period coinciding with intensive in situ measurements (EUREC4A-OA experiment). The Angular Momentum Eddy Detection and tracking Algorithm (AMEDA) detection algorithm was then employed to detect and track eddies in both fields. Subsequently, a particular NBC ring present in the region in January and February 2020 was examined. The comparison demonstrated that AIS data exhibited the precision and resolution necessary to effectively identify the NBC rings and smaller surrounding eddies, aligning well with other datasets such as in situ measurements, sea surface temperature (SST), and sea surface salinity (SSS) data. Moreover, we established that AIS data yielded accurate regional velocity fields, as evidenced by an analysis of energy spectra. Furthermore, our analysis revealed that AIS data captured aspects of eddy–eddy interactions which were not adequately depicted in AVISO fields. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (or GANs) are a specific deep learning architecture often used for different usages, such as data generation or image-to-image translation. In recent years, this structure has gained increased popularity and has been used in different fields. One area of expertise currently in vogue is the use of GANs to produce synthetic aperture radar (SAR) data, and especially expand training datasets for SAR automatic target recognition (ATR). In effect, the complex SAR image formation makes these kind of data rich in information, leading to the use of deep networks in deep learning-based methods. Yet, deep networks also require sufficient data for training. However, contrary to optical images, we generally do not have a substantial number of available SAR images because of their acquisition and labelling cost; GANs are then an interesting tool. Concurrently, how to improve explainability for SAR ATR deep neural networks and how to make their reasoning more transparent have been increasingly explored as model opacity deteriorates trust of users. This paper aims at reviewing how GANs are used with SAR images, but also giving perspectives on how GANs could be used to improve interpretability and explainability of SAR classifiers. [ABSTRACT FROM AUTHOR]

Submerged aquatic vegetation, also referred to as submerged macrophytes, provides important habitats and serves as a significant ecological indicator for assessing the condition of water bodies and for gaining insights into the impacts of climate change. In this study, we introduce a novel approach for the classification of submerged vegetation captured with bathymetric LiDAR (Light Detection And Ranging) as a basis for monitoring their state and change, and we validated the results against established monitoring techniques. Employing full-waveform airborne laser scanning, which is routinely used for topographic mapping and forestry applications on dry land, we extended its application to the detection of underwater vegetation in Lake Constance. The primary focus of this research lies in the automatic classification of bathymetric 3D LiDAR point clouds using a decision-based approach, distinguishing the three vegetation classes, (i) Low Vegetation, (ii) High Vegetation, and (iii) Vegetation Canopy, based on their height and other properties like local point density. The results reveal detailed 3D representations of submerged vegetation, enabling the identification of vegetation structures and the inference of vegetation types with reference to pre-existing knowledge. While the results within the training areas demonstrate high precision and alignment with the comparison data, the findings in independent test areas exhibit certain deficiencies that are likely addressable through corrective measures in the future. [ABSTRACT FROM AUTHOR]

Piping is a major factor contributing to river embankment breaches, particularly during flood season in small and medium rivers. To reduce the costs of earth rock embankment inspections, avoid the need for human inspectors and enable the quick and widespread detection of piping hazards, a UAV image-acquisition function was introduced in this study. Through the collection and analysis of thermal infrared and visible (TIR & V) images from several piping field simulation experiments, temperature increases, and diffusion centered on the piping point were discovered, so an automatic algorithm for piping identification was developed to capture this phenomenon. To verify the identification capabilities, the automatic identification algorithm was applied to detect potential piping hazards during the 2022 flooding of the Dingjialiu River, Liaoning, China. The algorithm successfully identified all five piping hazard locations, demonstrating its potential for detecting embankment piping. [ABSTRACT FROM AUTHOR]

The ionospheric quasi-periodic wave is a type of typical and common electromagnetic wave phenomenon occurring in extremely low-frequency (ELF) and very low-frequency ranges (VLF). These emissions propagate in a distinct whistler-wave mode, with varying periodic modulations of the wave intensity over time scales from several seconds to a few minutes. We developed an automatic detection model for the QP waves in the ELF band recorded by the China Seismo-Electromagnetic Satellite. Based on the 827 QP wave events, which were collected through visual screening from the electromagnetic field observations, an automatic detection model based on the Transformer architecture was built. This model, comprising 34.27 million parameters, was trained and evaluated. It achieved mean average precision of 92.3% on the validation dataset, operating at a frame rate of 39.3 frames per second. Notably, after incorporating the proton cyclotron frequency constraint, the model displayed promising performance. Its lightweight design facilitates easy deployment on satellite equipment, significantly enhancing the feasibility of on-board detection. [ABSTRACT FROM AUTHOR]

Sinkholes can cause significant damage to infrastructures, agriculture, and endanger lives in active karst regions like the Dead Sea’s eastern shore at Ghor Al-Haditha. The common sinkhole mapping methods often require costly high-resolution data and manual, time-consuming expert analysis. This study introduces an efficient deep learning model designed to improve sinkhole mapping using accessible satellite imagery, which could enhance management practices related to sinkholes and other geohazards in evaporite karst regions. The developed AI system is centered around the U-Net architecture. The model was initially trained on a high-resolution drone dataset (0.1 m GSD, phase I), covering 250 sinkhole instances. Subsequently, it was additionally fine-tuned on a larger dataset from a Pleiades Neo satellite image (0.3 m GSD, phase II) with 1038 instances. The training process involved an automated image-processing workflow and strategic layer freezing and unfreezing to adapt the model to different input scales and resolutions. We show the usefulness of initial layer features learned on drone data, for the coarser, more readily-available satellite inputs. The validation revealed high detection accuracy for sinkholes, with phase I achieving a recall of 96.79% and an F1 score of 97.08%, and phase II reaching a recall of 92.06% and an F1 score of 91.23%. These results confirm the model’s accuracy and its capability to maintain high performance across varying resolutions. Our findings highlight the potential of using RGB visual bands for sinkhole detection across different karst environments. This approach provides a scalable, cost-effective solution for continuous mapping, monitoring, and risk mitigation related to sinkhole hazards. The developed system is not limited only to sinkholes however, and can be naturally extended to other geohazards as well. Moreover, since it currently uses U-Net as a backbone, the system can be extended to incorporate super-resolution techniques, leveraging U-Net based latent diffusion models to address the smaller-scale, ambiguous geo-structures that are often found in geoscientific data.

Automatic modulation classification (AMC) based on deep learning (DL) is gaining increasing attention in dynamic spectrum access for 5G/6G wireless communications. However, inconsistent feature parameters between the training (source) and testing (target) data lead to performance degradation or even failure of existing DL-based AMC. The primary reason for this is the difficulty in obtaining sufficient labeled training data in the target domain. Therefore, we propose a novel cross-domain AMC algorithm based on multimodal information and transfer learning, utilizing abundant unlabeled target domain data. We achieve complementary gains by fusing multimodal information such as amplitude, phase, and spectrum, which are used to train a network. Additionally, we apply domain adversarial neural network technology from transfer learning to learn from a large number of unlabeled data samples in the target domain to address the issue of decreased accuracy in cross-domain AMC caused by differences in sampling rate, signal-to-noise ratio, and channel variations. Furthermore, we introduce class weight weighting and entropy weighting to solve the partial domain adaptation problem, considering that the target domain has fewer modulation signal classes than the source domain. Experimental results on two designed modulation datasets demonstrate improved performance gains, thus validating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

General synthetic aperture radar (SAR) image automatic target recognition (ATR) methods perform well under standard operation conditions (SOCs). However, they are not effective in extended operation conditions (EOCs). To improve the robustness of the ATR system under various EOCs, an ATR method for SAR images based on the scattering parameter Gaussian mixture model (GMM) is proposed in this paper. First, an improved active contour model (ACM) is used for target–background segmentation, which is more robust against noise than the constant false alarm rate (CFAR) method. Then, as the extracted attributed scattering center (ASC) is sensitive to noise and resolution, the GMM is constructed using the extracted ASC set. Next, the weighted Gaussian quadratic form distance (WGQFD) is adopted to measure the similarity of GMMs for the recognition task, thereby avoiding false alarms and missed alarms caused by the varying number of scattering centers. Moreover, adaptive aspect–frame division is employed to reduce the number of templates and improve recognition efficiency. Finally, based on the public measured MSTAR dataset, different EOCs are constructed under noise, resolution change, model change, depression angle change, and occlusion of different proportions. The experimental results under different EOCs demonstrate that the proposed method exhibits excellent robustness while maintaining low computation time. [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]

In the era of the first Industrial Revolution, many buildings were built with red bricks, and the heritage buildings built at that time are more than 100 years old. In these old heritage buildings, damage is bound to occur due to chemical and physical effects. Technologies such as automatic damage detection can effectively manage damage, but they can be affected by other categories present in heritage buildings. Therefore, this paper proposes a CNN algorithm that can automatically detect cracks and damage that occur in heritage buildings, as well as multi-label classification, such as doors, windows, arches, artwork, brick walls, stonewalls, and vents. A total of 2400 thermal infrared images are collected for 8 categories and automatic classification was performed using the CNN algorithm. The average precision and average sensitivity for the eight categories of heritage buildings are 97.72% and 97.43%, respectively. This paper defines the causes of misclassification as the following two causes: misclassification by multiple objects and misclassification by the perception of the CNN algorithm. [ABSTRACT FROM AUTHOR]

Aquatic vegetation is an important component of aquatic ecosystems; therefore, the classification and mapping of aquatic vegetation is an important aspect of lake management. Currently, the decision tree (DT) classification method based on spectral indices has been widely used in the extraction of aquatic vegetation data, but the disadvantage of this method is that it is difficult to fix the threshold value, which, in turn, affects the automatic classification effect. In this study, Sentinel-2 MSI data were used to produce a sample set (about 930 samples) of aquatic vegetation in four inland lakes (Lake Taihu, Lake Caohai, Lake Honghu, and Lake Dongtinghu) using the visual interpretation method, including emergent, floating-leaved, and submerged vegetation. Based on this sample set, a DL model (Res-U-Net) was used to train an automatic aquatic vegetation extraction model. The DL model achieved a higher overall accuracy, relevant error, and kappa coefficient (90%, 8.18%, and 0.86, respectively) compared to the DT method (79%, 23.07%, and 0.77) and random forest (78%,10.62% and 0.77) when utilizing visual interpretation results as the ground truth. When utilizing measured point data as the ground truth, the DL model exhibited accuracies of 59%, 78%, and 91% for submerged, floating-leaved, and emergent vegetation, respectively. In addition, the model still maintains good recognition in the presence of clouds with the influence of water bloom. When applying the model to Lake Honghu from January 2017 to October 2023, the obtained temporal variation patterns in the aquatic vegetation were consistent with other studies. The study in this paper shows that the proposed DL model has good application potential for extracting aquatic vegetation data.

Landslides are the most serious geological disaster in our country, causing economic losses. Because they go undetected, a large number of landslides that have caused disasters are not in the catalogue. At present, Interferometric Synthetic Aperture Radar (InSAR) has been widely used in the identification of landslides. However, it is time-consuming, inefficient, etc., to survey landslides throughout our large country. In the context of massive SAR data, this problem is more obvious. Therefore, based on the current technique of using differential interferogram phase gradient stacking to avoid phase unwrapping errors, a landslide phase gradient dataset has been constructed. To validate the dataset’s effectiveness and applicability, deep learning methods were introduced, applying the dataset to four networks: U-Net, Attention-Unet, Bisenet v2, and Deeplab v3. The results indicate that the phase gradient dataset performs well across different models, with the Attention-Unet network demonstrating the best performance. Specifically, the precision, recall, and accuracy on the test dataset were 0.8771, 0.8712, and 0.9834, respectively, and the accuracy on the validation dataset was 0.8523. Finally, in this paper, the model is applied to landslide identification in Gansu Province, China, during 2022-2023, and a total of 1882 landslides are found. These landslides are mainly concentrated in the south of Gansu Province, where the terrain is relatively undulating. The results show that this method can quickly and accurately realize landslide automatic identification in a wide area and provide technical support for large-scale landslide disaster surveys.

This study introduces a new software, cLASpy_T, that helps design models for the automatic 3D point cloud classification of coastal environments. This software is based on machine learning algorithms from the scikit-learn library and can classify point clouds derived from LiDAR or photogrammetry. Input data can be imported via CSV or LAS files, providing a 3D point cloud, enhanced with geometric features or spectral information, such as colors from orthophotos or hyperspectral data. cLASpy_T lets the user run three supervised machine learning algorithms from the scikit-learn API to build automatic classification models: RandomForestClassifier, GradientBoostingClassifier and MLPClassifier. This work presents the general method for classification model design using cLASpy_T and the software's complete workflow with an example of photogrammetry point cloud classification. Four photogrammetric models of a coastal dike were acquired on four different dates, in 2021. The aim is to classify each point according to whether it belongs to the 'sand' class of the beach, the 'rock' class of the riprap, or the 'block' class of the concrete blocks. This case study highlights the importance of adjusting algorithm parameters, selecting features, and the large number of tests necessary to design a classification model that can be generalized and used in production. [ABSTRACT FROM AUTHOR]

To achieve automatic recognition of lightning images, which cannot easily be handled using the existing methods and still requires significant human resources, we propose a lightning image dataset and a preprocessing method. The lightning image data over five months were collected using a camera based on two optical observation stations, and then a series of batch labeling methods were applied, which greatly reduced the workload of subsequent manual labeling, and a dataset containing more than 30,000 labeled samples was established. Considering that lightning varies rapidly over time, we propose a time sequence composite (TSC) preprocessing method that inputs lightning’s time-varying characteristics into a model for better recognition of lightning images. The TSC method was evaluated through an experiment on four backbones, and it was found that this preprocessing method enhances the classification performance by 40%. The final trained model could successfully distinguish between the “lightning” and “non-lightning” samples, and a recall rate of 86.5% and a false detection rate of 0.2% were achieved.

Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level and capabilities of military and civilian automation. Although RATR has been successfully applied in some aspects, the complete theoretical system has not been established. At present, deep learning algorithms have received a lot of attention and have emerged as potential and feasible solutions in RATR. This paper mainly reviews related articles published between 2010 and 2022, which corresponds to the period when deep learning methods were introduced into RATR research. In this paper, the current research status of radar target characteristics is summarized, including motion, micro-motion, one-dimensional, and two-dimensional characteristics, etc. This paper reviews the progress of deep learning methods in the feature extraction and recognition of radar target characteristics in recent years, including space, air, ground, sea-surface targets, etc. Due to more and more attention and research results published in the past few years, it is hoped that this review can provide potential guidance for future research and application of deep learning in fields related to RATR. [ABSTRACT FROM AUTHOR]

The polar regions of Mars, including the South and North Poles, are crucial for studying Martian climate and geological history, as they contain the largest reservoir of subsurface water ice. This study introduces a new approach for reflector detection, which includes radargram denoising to effectively enhance the signal of underground reflectors, peak detection to extract the positions of subsurface stratification from the radar echoes, and peak points connection to form continuous layers. The mapped enhancement denoising process involves a linear brightness adjustment and a fourth-order diffusion equation to enhance the signal of the subsurface layers for effective detection. The subsurface detection extracts the surface and subsurface peak points based on a peak detection algorithm, while using locally window-enhanced peak filtering and Kullback–Leibler (KL) divergence mapping to filter out non-stratified peak points. Finally, the layered connection process uses the proximity parameter to connect peak points in the same layer. Applied to multiple SHARAD (Shallow Radar) images at the Martian poles, this algorithm demonstrated a false detection rate below 5%. Compared to other methods, this method has a missed detection rate of less than 5% and, additionally, exhibits fewer discontinuities in layer connectivity. Therefore, this algorithm shows exceptional proficiency and applicability in analyzing the complex subsurface structures of the Martian polar regions.

With the prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning-based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning-based methods can achieve outstanding recognition performance on the condition that an abundance of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Therefore, SAR target recognition in the situation of few-shot, where only a scarce few labeled samples are available, is a fundamental problem that needs to be solved. In this paper, a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) is put forward to specially tackle the few-shot SAR ATR tasks. To be specific, a feature-extraction network based on a customized and region-aware convolution is first developed. This network can adaptively adjust convolutional kernels and their receptive fields according to each SAR image's own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, an enhanced prototypical network is proposed. This network can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset, the OpenSARShip dataset, and the SAMPLE+ dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) technology has become one of the mainstream techniques for active landslide identification over a large area. However, the method for interpreting anomalous deformation areas derived from InSAR data is still mainly manual delineation through human–computer interaction. This study focuses on using a deep learning semantic segmentation model to identify the boundaries of anomalous deformation areas automatically. We experimented with the delineation results based on an InSAR deformation map, hot spot map, and different combinations of topographic datasets to build the optimal model. The result indicates that the hot spot map, aspect, and Google Earth image as input features based on the U-Net model can achieve the best performance, with the precision, recall, F1 score, and intersection over union (IoU) being 0.822, 0.835, 0.823, and 0.705, respectively. Our method promotes the development of identifying active landslides using InSAR technology automatically and rapidly at a regional scale. Moreover, applying a new method for automatically and rapidly identifying potential landslides in susceptible areas is necessary for landslide hazard mitigation and risk management. [ABSTRACT FROM AUTHOR]

Automatic landslide classification based on digital elevation models has become a powerful complementary tool to field mapping. Many studies focus on the automatic classification of landslides' geomorphological features, such as their steep main scarps, but in many cases, the scarps and other morphological features are difficult for algorithms to detect. In this study, we performed an automatic classification of different litho-geomorphological units to differentiate slope mass movements in field maps by using Maximum Likelihood Classification. The classification was based on high-resolution lidar-derived DEM of the Vipava Valley, SW Slovenia. The results show an improvement over previous approaches as we used a blended image (VAT, which included four different raster layers with different weights) along with other common raster layers for morphometric analysis of the surface (e.g., slope, elevation, aspect, TRI, curvature, etc.). The newly created map showed better classification of the five classes we used in the study and recognizes alluvial deposits, carbonate cliffs (including landslide scarps), carbonate plateaus, flysch, and slope deposits better than previous studies. Multivariate statistics recognized the VAT layer as the most important layer with the highest eigenvalues, and when combined with Aspect and Elevation layers, it explained 90% of the total variance. The paper also discusses the correlations between the different layers and which layers are better suited for certain geomorphological surface analyses. [ABSTRACT FROM AUTHOR]

InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter's experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation. [ABSTRACT FROM AUTHOR]

Sinkholes can cause significant damage to infrastructures, agriculture, and endanger lives in active karst regions like the Dead Sea's eastern shore at Ghor Al-Haditha. The common sinkhole mapping methods often require costly high-resolution data and manual, time-consuming expert analysis. This study introduces an efficient deep learning model designed to improve sinkhole mapping using accessible satellite imagery, which could enhance management practices related to sinkholes and other geohazards in evaporite karst regions. The developed AI system is centered around the U-Net architecture. The model was initially trained on a high-resolution drone dataset (0.1 m GSD, phase I), covering 250 sinkhole instances. Subsequently, it was additionally fine-tuned on a larger dataset from a Pleiades Neo satellite image (0.3 m GSD, phase II) with 1038 instances. The training process involved an automated image-processing workflow and strategic layer freezing and unfreezing to adapt the model to different input scales and resolutions. We show the usefulness of initial layer features learned on drone data, for the coarser, more readily-available satellite inputs. The validation revealed high detection accuracy for sinkholes, with phase I achieving a recall of 96.79% and an F1 score of 97.08%, and phase II reaching a recall of 92.06% and an F1 score of 91.23%. These results confirm the model's accuracy and its capability to maintain high performance across varying resolutions. Our findings highlight the potential of using RGB visual bands for sinkhole detection across different karst environments. This approach provides a scalable, cost-effective solution for continuous mapping, monitoring, and risk mitigation related to sinkhole hazards. The developed system is not limited only to sinkholes however, and can be naturally extended to other geohazards as well. Moreover, since it currently uses U-Net as a backbone, the system can be extended to incorporate super-resolution techniques, leveraging U-Net based latent diffusion models to address the smaller-scale, ambiguous geo-structures that are often found in geoscientific data. [ABSTRACT FROM AUTHOR]

Instance segmentation is pivotal in remote sensing image (RSI) analysis, aiding in many downstream tasks. However, annotating images with pixel-wise annotations is time-consuming and laborious. Despite some progress in automatic annotation, the performance of existing methods still needs improvement due to the high precision requirements for pixel-level annotation and the complexity of RSIs. With the support of large-scale data, some foundational models have made significant progress in semantic understanding and generalization capabilities. In this paper, we delve deep into the potential of the foundational models in automatic annotation and propose a training-free automatic annotation method called DiffuPrompter, achieving pixel-level automatic annotation of RSIs. Extensive experimental results indicate that the proposed method can provide reliable pseudo-labels, significantly reducing the annotation costs of the segmentation task. Additionally, the cross-domain validation experiments confirm the powerful effectiveness of large-scale pseudo-data in improving model generalization performance. [ABSTRACT FROM AUTHOR]

This study introduces a new software, cLASpy_T, that helps design models for the automatic 3D point cloud classification of coastal environments. This software is based on machine learning algorithms from the scikit-learn library and can classify point clouds derived from LiDAR or photogrammetry. Input data can be imported via CSV or LAS files, providing a 3D point cloud, enhanced with geometric features or spectral information, such as colors from orthophotos or hyperspectral data. cLASpy_T lets the user run three supervised machine learning algorithms from the scikit-learn API to build automatic classification models: RandomForestClassifier, GradientBoostingClassifier and MLPClassifier. This work presents the general method for classification model design using cLASpy_T and the software’s complete workflow with an example of photogrammetry point cloud classification. Four photogrammetric models of a coastal dike were acquired on four different dates, in 2021. The aim is to classify each point according to whether it belongs to the ‘sand’ class of the beach, the ‘rock’ class of the riprap, or the ‘block’ class of the concrete blocks. This case study highlights the importance of adjusting algorithm parameters, selecting features, and the large number of tests necessary to design a classification model that can be generalized and used in production.

SAR-ATR (synthetic aperture radar-automatic target recognition) is a hot topic in remote sensing. This work suggests a few-shot target recognition approach (FTL) based on the concept of transfer learning to accomplish accurate target recognition of SAR images in a few-shot scenario since the classic SAR ATR method has significant data reliance. At the same time, the strategy introduces a model distillation method to improve the model's performance further. This method is composed of three parts. First, the data engine, which uses the style conversion model and optical image data to generate image data similar to SAR style and realize cross-domain conversion, can effectively solve the problem of insufficient training data of the SAR image classification model. Second is model training, which uses SAR image data sets to pre-train the model. Here, we introduce the deep Brownian distance covariance (Deep BDC) pooling layer to optimize the image feature representation so that the model can learn the image representation by measuring the difference between the joint feature function of the embedded feature and the edge product. Third, model fine-tuning, which freezes the model structure, except the classifier, and fine-tunes it by using a small amount of novel data. The knowledge distillation approach is also introduced simultaneously to train the model repeatedly, sharpen the knowledge, and enhance model performance. According to experimental results on the MSTAR benchmark dataset, the proposed method is demonstrably better than the SOTA method in the few-shot SAR ATR issue. The recognition accuracy is about 80% in the case of 10-way 10-shot. [ABSTRACT FROM AUTHOR]

Using intelligent optimization algorithms to retrieve atmospheric duct parameters by monitoring automatic identification system (AIS) signals at sea is a new passive remote sensing technology for atmospheric ducts. To thoroughly compare and analyze the inversion results of different intelligent optimization algorithms and optimize the parameters of the algorithms, this study considered a simulated atmospheric duct environment for atmospheric duct inversion using the genetic, simulated annealing, and particle swarm optimization (PSO) algorithms. The results indicated that the PSO algorithm exhibited the best inversion performance. The inversion results of the simulated annealing particle swarm optimization (SAPSO) and PSO algorithms under different inversion parameters were further statistically analyzed, and the atmospheric duct parameters were obtained from measured AIS signals based on the SAPSO algorithm. The inversion results verified the effectiveness of the proposed algorithm, and they continuously improved with additional calculations in the inversion algorithm. However, the changing trend gradually slowed. Therefore, in practical applications, the inversion time consumption should be balanced with the inversion effect to optimize the inversion parameters. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

To achieve automatic recognition of lightning images, which cannot easily be handled using the existing methods and still requires significant human resources, we propose a lightning image dataset and a preprocessing method. The lightning image data over five months were collected using a camera based on two optical observation stations, and then a series of batch labeling methods were applied, which greatly reduced the workload of subsequent manual labeling, and a dataset containing more than 30,000 labeled samples was established. Considering that lightning varies rapidly over time, we propose a time sequence composite (TSC) preprocessing method that inputs lightning's time-varying characteristics into a model for better recognition of lightning images. The TSC method was evaluated through an experiment on four backbones, and it was found that this preprocessing method enhances the classification performance by 40%. The final trained model could successfully distinguish between the "lightning" and "non-lightning" samples, and a recall rate of 86.5% and a false detection rate of 0.2% were achieved. [ABSTRACT FROM AUTHOR]

The analysis and preservation of the cultural heritage sites are critical for maintaining their historical and architectural integrity, as they can be damaged by various factors, including climatic, geological, geomorphological, and human actions. Based on this, the present study proposes a semi-automatic and non-learning-based method for detecting degraded surfaces within cultural heritage sites by integrating UAV, photogrammetry, and 3D data analysis. A 20th-century fortification from Romania was chosen as the case study due to its physical characteristics and state of degradation, making it ideal for testing the methodology. Images were collected using UAV and terrestrial sensors and processed to create a detailed 3D point cloud of the site. The developed pipeline effectively identified degraded areas, including cracks and material loss, with high accuracy. The classification and segmentation algorithms, including K-means clustering, geometrical features, RANSAC, and FACETS, improved the detection of destructured areas. The combined use of these algorithms facilitated a detailed assessment of the structural condition. This integrated approach demonstrated that the algorithms have the potential to support each other in minimizing individual limitations and accurately identifying degraded surfaces. Even though some limitations were observed, such as the potential for the overestimation of false negatives and positives areas, the damaged surfaces were extracted with high precision. The methodology proved to be a practical and economical solution for cultural heritage monitoring and conservation, offering high accuracy and flexibility. One of the greatest advantages of the method is its ease of implementation, its execution speed, and the potential of using entirely open-source software. This approach can be easily adapted to various heritage sites, significantly contributing to their protection and valorization.

This paper aims to analyze the North Brazil Current (NBC) rings during the initial 5 months of 2020 using surface currents derived from Automatic Identification System (AIS) data in comparison with altimetry-based Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO) current fields. The region of NBC rings is characterized by relatively high marine traffic, facilitating an accurate current estimation. Our investigation primarily focused on a brief period coinciding with intensive in situ measurements (EUREC4A-OA experiment). The Angular Momentum Eddy Detection and tracking Algorithm (AMEDA) detection algorithm was then employed to detect and track eddies in both fields. Subsequently, a particular NBC ring present in the region in January and February 2020 was examined. The comparison demonstrated that AIS data exhibited the precision and resolution necessary to effectively identify the NBC rings and smaller surrounding eddies, aligning well with other datasets such as in situ measurements, sea surface temperature (SST), and sea surface salinity (SSS) data. Moreover, we established that AIS data yielded accurate regional velocity fields, as evidenced by an analysis of energy spectra. Furthermore, our analysis revealed that AIS data captured aspects of eddy–eddy interactions which were not adequately depicted in AVISO fields.

Generative adversarial networks (or GANs) are a specific deep learning architecture often used for different usages, such as data generation or image-to-image translation. In recent years, this structure has gained increased popularity and has been used in different fields. One area of expertise currently in vogue is the use of GANs to produce synthetic aperture radar (SAR) data, and especially expand training datasets for SAR automatic target recognition (ATR). In effect, the complex SAR image formation makes these kind of data rich in information, leading to the use of deep networks in deep learning-based methods. Yet, deep networks also require sufficient data for training. However, contrary to optical images, we generally do not have a substantial number of available SAR images because of their acquisition and labelling cost; GANs are then an interesting tool. Concurrently, how to improve explainability for SAR ATR deep neural networks and how to make their reasoning more transparent have been increasingly explored as model opacity deteriorates trust of users. This paper aims at reviewing how GANs are used with SAR images, but also giving perspectives on how GANs could be used to improve interpretability and explainability of SAR classifiers.

Crop recognition with high accuracy at a large scale is hampered by the spatial heterogeneity of crop growth characteristics under the complex influence of environmental conditions. With the aim to automatically realize large-scale crop classification with high accuracy, this study proposes an automatic crop classification strategy considering spatial heterogeneity (ACCSH) by combining the geographic detector technique, random forest average accuracy model, and random forest classification model. In ACCSH, spectral and textural indexes that can quantify crop growth characteristics and environmental variables with potential driving effects are first calculated. Next, an adaptive spatial heterogeneity mining method based on the geographic detector technique is proposed to mine spatial homogeneous zones adaptively with significant differentiation of crop growth characteristics. Subsequently, in view of the differences in crop growth characteristics and key classification indexes between spatial homogeneous zones, correlation analysis, and random forest average accuracy are combined to optimize classification indexes independently within each zone. Finally, random forest is used to classify the target crop in each spatial homogeneous zone separately. The proposed ACCSH is applied to automatically recognize crop types, specifically wheat and corn, in northern France. Results show that kappa coefficients of wheat and corn using ACCSH are 15% and 26% higher than those of classifications at the global scale, respectively. In addition, the index optimization strategy in ACCSH shows apparent superiority. Kappa coefficients of wheat and corn are 5–18% and 9–42% higher than those of classifications based on non-optimized indexes, respectively. In general, ACCSH can automatically realize crop classification with a high precision that suggests its reliability.

With the capability of capturing a target's two-dimensional information, Inverse Synthetic Aperture Radar (ISAR) imaging is widely used in Radar Automatic Target Recognition. However, changes in the ship target's attitude can lead to the scatterers' rotation, occlusion, and angle glint, reducing the accuracy of ISAR image recognition. To solve this problem, we proposed a Triangle Preserving level-set-assisted Triangle-Points Affine Transform Reconstruction (TP-TATR) for ISAR ship target recognition. Firstly, three geometric points as initial information were extracted from the preprocessed ISAR images based on the ship features. Combined with these points, the Triangle Preserving level-set (TP) method robustly extracted the fitting triangle of targets depending on the intrinsic structure of the ship target. Based on the extracted triangle, the TP-TATR adjusted all the ship targets from the training and test data to the same attitude, thereby alleviating the attitude sensitivity. Finally, we created templates by averaging the adjusted training data and matched the test data with the templates for recognition. Experiments based on the simulated and measured data indicate that the accuracies of the TP-TATR method are 87.70% and 90.03%, respectively, which are higher than those of the contrast algorithms and have a statistical difference. These demonstrate the effectiveness and robustness of our proposed TP-TATR method. [ABSTRACT FROM AUTHOR]

Tweek signals are a special type of VLF (very low frequency) pulse, originally produced by lightning discharge, which becomes dispersive after repetitive bounces within the waveguide between the Earth’s surface and lower ionosphere. As such, tweek signals carry critical information about the region near the reflection height of the VLF waves, namely the D-region ionosphere. Although tweek measurements have been widely utilized in studies of the D-region ionosphere and lightning discharge, few statistical studies have been conducted, mainly due to the difficulty of manually identifying tweek signals from the enormous amount of VLF data with heavy noise. Considering the importance of tweek signals and the lack of a high-precision detection model, in this study, we propose a method to automatically and accurately pick out tweek signals from VLF measurements. This method is explicitly developed based on the you only look once (YOLO) model and a post-tracing process. Using a total of 2495 randomly selected VLF spectrogram images as the testing set, we evaluated the performance of this method. The precision and recall are found to be 92.0% and 89.2% for the first-order mode, and 97.5% and 86.7% for the first-two-order mode tweek, respectively. The time needed to process 10-s VLF measurements with a cadence of 4 μs is only 6.5 s, allowing for identifying the tweek signals from continuous VLF measurements in real time. Therefore, this method represents a reliable means to automatically detect tweek signals and enables the opportunity to statistically investigate the D-region ionosphere and lightning discharge via these signals.

Orbit error is one of the primary error sources of interferometric synthetic aperture radar (InSAR) and differential InSAR (D-InSAR) measurements, arising from inaccurate orbit determination of SAR platforms. Typically, orbit error in the interferogram can be estimated using polynomial models. However, correcting for orbit errors with significant time-varying characteristics presents two main challenges: (1) the complexity and variability of the azimuth time-varying orbit errors make it difficult to accurately model them using a set of polynomial coefficients; (2) existing patch-based polynomial models rely on empirical segmentation and overlook the time-varying characteristics, resulting in residual orbital error phase. To overcome these problems, this study proposes an automated block adjustment framework for estimating time-varying orbit errors, incorporating the following innovations: (1) the differential interferogram is divided into several blocks along the azimuth direction to model orbit error separately; (2) automated segmentation is achieved by extracting morphological features (i.e., peaks and troughs) from the azimuthal profile; (3) a block adjustment method combining control points and connection points is proposed to determine the model coefficients of each block for the orbital error phase estimation. The feasibility of the proposed method was verified by repeat-pass L-band spaceborne and P-band airborne InSAR data, and finally, the InSAR digital elevation model (DEM) was generated for performance evaluation. Compared with the high-precision light detection and ranging (LiDAR) elevation, the root mean square error (RMSE) of InSAR DEM was reduced from 18.27 m to 7.04 m in the spaceborne dataset and from 7.83~14.97 m to 3.36~6.02 m in the airborne dataset. Then, further analysis demonstrated that the proposed method outperforms existing algorithms under single-baseline and single-polarization conditions. Moreover, the proposed method is applicable to both spaceborne and airborne InSAR data, demonstrating strong versatility and potential for broader applications.

This work presents two pre-processing patches to automatically correct the residual local misalignment of datasets acquired by very/extremely high resolution (VHR/EHR) satellite multispectral (MS) scanners, one for, e.g., GeoEye-1 and Pléiades, featuring two separate instruments for MS and panchromatic (Pan) data, the other for WorldView-2/3 featuring three instruments, two of which are visible and near-infra-red (VNIR) MS scanners. The misalignment arises because the two/three instruments onboard GeoEye-1 / WorldView-2 (four onboard WorldView-3) share the same optics and, thus, cannot have parallel optical axes. Consequently, they image the same swath area from different positions along the orbit. Local height changes (hills, buildings, trees, etc.) originate local shifts among corresponding points in the datasets. The latter would be accurately aligned only if the digital elevation surface model were known with sufficient spatial resolution, which is hardly feasible everywhere because of the extremely high resolution, with Pan pixels of less than 0.5 m. The refined co-registration is achieved by injecting the residue of the multivariate linear regression of each scanner towards lowpass-filtered Pan. Experiments with two and three instruments show that an almost perfect alignment is achieved. MS pansharpening is also shown to greatly benefit from the improved alignment. The proposed alignment procedures are real-time, fully automated, and do not require any additional or ancillary information, but rely uniquely on the unimodality of the MS and Pan sensors.

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.

The modulation classification technology for radar intra-pulse signals is important in the electronic countermeasures field. As the high quality labeled radar signals are difficult to be captured in the real applications, the signal modulation classification base on the limited number of labeled samples is playing a more and more important role. To relieve the requirement of the labeled samples, many self-supervised learning (SeSL) models exist. However, as they cannot fully explore the information of the labeled samples and rely significantly on the unlabeled samples, highly time-consuming processing of the pseudo-labels of the unlabeled samples is caused. To solve these problems, a supervised learning (SL) model, using the contrastive learning (CL) method (SL-CL), is proposed in this paper, which achieves a high classification accuracy, even adopting limited number of labeled training samples. The SL-CL model uses a two-stage training structure, in which the CL method is used in the first stage to effectively capture the features of samples, then the multilayer perceptron is applied in the second stage for the classification. Especially, the supervised contrastive loss is constructed to fully exploring the label information, which efficiently increases the classification accuracy. In the experiments, the SL-CL outperforms the comparison models in the situation of limited number of labeled samples available, which reaches 94% classification accuracy using 50 samples per class at 5 dB SNR.