IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献

{"title":"RipScout: Realtime ML-Assisted Rip Current Detection and Automated Data Collection Using UAVs","authors":"Fahim Hasan Khan;Donald Stewart;Akila de Silva;Ashleigh Palinkas;Gregory Dusek;James Davis;Alex Pang","doi":"10.1109/JSTARS.2025.3543695","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3543695","url":null,"abstract":"This article presents RipScout, a system for realtime rip current detection and data collection using drones equipped with machine learning (ML). No internet connection is required. RipScout achieves realtime performance by using lightweight ML models that fit the constraints of limited mobile computing resources with the drone controller. We compared different ML models trained to detect either one or two types of rip currents. The best model was then selected for RipScout. The system was evaluated with three ML models, with the EfficientDet D2 model achieving the highest accuracy of 93.1% for multiclass detection while maintaining realtime processing at an average speed of 17 frames per second. When a rip is detected along a flight path, the drone hovers in place and collects a video clip of a predefined length, followed by circling around the detected rip using prespecified radii and heights to collect video samples from different vantage points and elevations. An important benefit of RipScout is that the collection of rip current data can be performed by drone operators who are not familiar with rip currents. We conducted field tests and found that the proposed system allows data to be collected four times faster than without it while improving accuracy. As a by-product of the field experiments, we also provide a new rip current dataset. Such a multiviewpoint dataset can be used to improve rip current detection, especially from lower elevations and different orientations.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7742-7755"},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10892036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Patch-Wise Mechanism for Enhancing Sparse Radar Echo Extrapolation in Precipitation Nowcasting","authors":"Yueting Wang;Hou Jiang;Tang Liu;Ling Yao;Chenghu Zhou","doi":"10.1109/JSTARS.2025.3543386","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3543386","url":null,"abstract":"Precipitation nowcasting is pivotal for disaster prevention, urban planning, and various societal applications. Although deep learning-based radar echo extrapolation methods are widely adopted, their effectiveness is limited by challenges in handling sparse echo sequences with low pixel proportions. This limitation hinders accurate predictions of low-frequency heavy rainfall and localized precipitation events. To address these issues, a novel Patch-wise (PW) mechanism is proposed in this study. Specifically, radar echo frames are divided into patches, which are then encoded into sequences and processed using a local attention mechanism to enhance critical regional information extraction. Furthermore, multiscale convolutions tailored to the patch scale are employed to expand the receptive field, and a convolutional block attention module is introduced to capture the spatiotemporal dynamics of sparse echoes and intense rainfall. These improvements enable extraction of sparse spatiotemporal features in a PW manner, enhancing prediction accuracy for regional sparse precipitation. Experiments across Chinese regions demonstrate the effectiveness of the PW mechanism. Notably, integrating the PW into PredRNN model yields improvements in the critical success index by 1.24%, 1.37%, and 12.59% for precipitation intensity thresholds of 20, 30, and 45 dBZ, respectively. Spatial visualizations of radar echoes reveal PW's superior in predicting localized and intense precipitation. This study is expected to advance regional precipitation nowcasting by offering both practical insights and methodological innovations.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8138-8150"},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891732","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advances in Deep-Learning-Based SAR Image Target Detection and Recognition","authors":"Ping Lang;Xiongjun Fu;Jian Dong;Huizhang Yang;Junjun Yin;Jian Yang;Marco Martorella","doi":"10.1109/JSTARS.2025.3543531","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3543531","url":null,"abstract":"Synthetic aperture radar (SAR) image target detection and recognition (SAR-TDR) tasks have become research hot spots in the remote sensing application. These targets include ships, vehicles, aircraft, oil tanks, bridges, and so on. However, with the rapid development of SAR technology and increasingly complex electromagnetic environment, complex characteristics of SAR images bring severe challenges to the accurate SAR-TDR via traditional physical models or manual feature-extraction-based machine learning methods. In recent years, deep learning (DL), as a powerful automatic feature extraction algorithm, has been widely used in the computer vision domain. More specifically, DL has also been introduced into the SAR-TDR tasks and has effectively achieved good performance in terms of accuracy, real-time processing, etc. With the rapid development of DL, SAR image processing, and practical requirements of SAR-TDR in civilian and military domains, it is crucial to conduct a systematic survey on SAR-TDR in the past few years. In this survey article, we mainly conduct a systematic overview of DL-based SAR-TDR literature on two tasks, i.e., target recognition (e.g., ground vehicles, ships, and aircraft) and target detection (e.g., ships, aircraft, change detection, sea surface oil spills, and oil tanks). More specifically, our related works about these topics are also presented to verify the effectiveness of DL-based methods. First, several DL methods (e.g., convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks), commonly used in SAR-TDR, are briefly introduced. Then, a systematic review of DL-based SAR-TDR (including our related works) is presented. Finally, the current challenges and future possible research directions are deeply analyzed and discussed.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6884-6915"},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10892079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote Sensing Small Object Detection Based on Multicontextual Information Aggregation","authors":"Jingyu Wang;Mingrui Ma;Pengfei Huang;Shaohui Mei;Liang Zhang;Hongmei Wang","doi":"10.1109/JSTARS.2025.3543189","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3543189","url":null,"abstract":"Due to wide field of view and background confusion, remote sensing objects are small and densely packed, hence commonly used detection methods detecting small objects are not satisfactory. In this article, we propose the multicontextual information aggregation YOLO (MCIA-YOLO) method, combining three novel modules to effectively aggregate multicontextual information across channels, depths, and pixels. First, the channel-spatial information aggregation module assembles spatial global features pursuant to channel contextual information, increasing the density of key information. Second, the shallow-deep information sparse aggregation module applies a sparse cross self-attention mechanism. By sparsely correlating long-range dependency information across different regions, the representation capability of a small target is enhanced while removing redundant information. Third, to enrich local multiscale features and better identify dense targets, multiscale weighted aggregation module convolves multireceptive field information and performs weighted fusion. Our method demonstrates satisfactory performance on dataset VisDrone2019, UAVDT, and NWPU VHR-10, especially in small objects detection, surpassing several state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8248-8260"},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Guided Machine Learning-Based Forward-Modeling of Radar Observables: A Case Study on Sentinel-1 Observations of Corn-Fields","authors":"Tina Nikaein;Paco Lopez-Dekker","doi":"10.1109/JSTARS.2025.3543238","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3543238","url":null,"abstract":"Artificial neural networks have the potential to model the interaction of radar signals with vegetation but often do not follow the physical rules. This article aims to develop a new physics-guided machine learning approach that combines neural networks and physics-based models to leverage their complementary strengths and improve the modeling of physical processes. We propose a data-driven framework to model synthetic aperture radar observables by incorporating physical knowledge in two ways: through the network architecture and the loss function. A key aspect of our approach is its ability to integrate knowledge encoded in physics-based models. The results show that by using scientific knowledge to guide the construction and learning of the neural network, we can provide a framework with better generalizability and stability.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6492-6502"},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891699","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Mountainous DSM Accuracy Through an Innovative Opposite-Side Radargrammetry Algorithm","authors":"Jian Wang;Huiming Chai;Xiaoshuai Li;Xiaolei Lv","doi":"10.1109/JSTARS.2025.3543430","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3543430","url":null,"abstract":"Radargrammetry is a critical technique for generating a high-resolution digital surface model (DSM). In radargrammetry, a large intersection angle between stereo images leads to higher elevation accuracy. Traditional radargrammetry often utilizes same-side stereo SAR images with a small intersection angle. Opposite-side radargrammetry can achieve higher accuracy DSM with its large intersection angle. However, dense stereo matching of opposite-side images is challenging due to the different orbit directions of the satellites, especially in mountainous areas. To address this issue, we propose an innovative indirect SAR image matching algorithm for generating opposite-side radargrammetric mountainous DSM. First, a SAR image simulation method is proposed to connect the opposite-side SAR images using slope and orbit information. Second, a triangle affine matching algorithm is developed to match the simulated SAR and real SAR images based on feature points. Then, the opposite-side SAR images can be matched according to the proposed algorithm. Finally, the stereo positioning method is introduced to obtain the geographic coordinates point cloud and the final DSM. The proposed method is validated using a spaceborne GaoFen-3 dataset over the mountainous area in Omaha, Nebraska, USA. The generated DSM is compared against open-source light detection and ranging data from the U.S. Geological Survey. The results demonstrate that the proposed method achieves a root mean square error of 6.41 m, representing a 24.2% and 20.1% improvement compared to the same-side radargrammetry method and the existing opposite-side radargrammetry method, respectively.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6641-6653"},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10892082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Integrated Feature Fusion of Handcrafted and Deep Features for Rice Blast Resistance Identification Using UAV Imagery","authors":"Peng Zhang;Zibin Zhou;Huasheng Huang;Yuanzhu Yang;Xiaochun Hu;Jiajun Zhuang;Yu Tang","doi":"10.1109/JSTARS.2025.3543190","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3543190","url":null,"abstract":"Nowadays, the combination of UAV remote sensing and deep learning has facilitated effective high-throughput field phenotyping for rice-blast-resistant breeding. However, breeding practices can hardly provide sufficient samples for each category due to the limitations of germplasm resources, which may cause data insufficiency and class imbalance. In addition, foliar and lesion details are often difficult to identify in UAV images due to the limitation of spatial resolution. As a result, the application of deep learning can lead to overfitting, as the model may struggle to acquire discriminative features. While previous studies have attempted to combine handcrafted and deep features to address problems with data insufficiency and class imbalances, image degradation still prevents the network from learning efficient representations for disease identification. To address these issues, this article proposes a hybrid integrated feature fusion (HIFF) method, in which a novel handcrafted-design-guided convolutional neural network module was employed to alleviate the problem of image degradation. Both handcrafted and deep learning branches were integrated in an end-to-end structure and applied to rice blast resistance identification. The proposed method was carefully evaluated using an ablation study, and the comparisons with state-of-the-art deep learning and feature fusion methods were conducted to demonstrate its superiority. Experimental results showed that the HIFF model outperformed mainstream methods by 0.0353 in F1-score and 0.0488 in accuracy on the practical rice-blast-resistant breeding applications. As such, the proposed method could be used to accelerate the process of rice-blast-resistant breeding.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7304-7317"},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891690","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSTCNet: Toward Generalization Improving for Multiframe Infrared Small Target Detection","authors":"Ruining Cui;Na Li;Junfu Liu;Huijie Zhao","doi":"10.1109/JSTARS.2025.3542617","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542617","url":null,"abstract":"Multiframe infrared small target detection plays an important role in various fields, especially in remote sensing. In continuous-frame infrared small target videos, factors such as the background change with the movement of the target. These changes lead to differences between the data distribution in actual application scenarios and the training scenarios. Existing deep learning methods are mostly designed for fixed scenarios. When facing scenarios with complex backgrounds and diverse changes, the generalization performance of the model is insufficient, leading to a decrease in detection accuracy and an increase in false alarms rate. To solve the problems mentioned above, combining the concept of domain generalization (DG) in transfer learning, we propose a multiscale spatio-temporal feature combined network (MSTCNet). First, we utilize the advantages of convolutional neural networks and recurrent neural networks, integrating them to build a high-performance structure. In addition, to further enhance generalization performance, we designed a selective physical information fusion (SPIF) module based on domain-invariant representation learning. This module enhances domain-invariant infrared small target features and reduces the impact of other irrelevant interferences. By integrating wavelet transform within the neural network, along with spatial attention and contrastive learning, SPIF strengthens domain-invariant features crucial for the task. Finally, in the experimental part, we adopt the DG verification method, dividing the dataset into different source domains and target domains for experimental verification. We verified the generalization performance of the proposed MSTCNet on two different datasets (IDGA and DTBA), and the experimental results confirmed the practicality and effectiveness of our method.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8416-8437"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MCDiff: A Multilevel Conditional Diffusion Model for PolSAR Image Classification","authors":"Qingyi Zhang;Xiaoxiao Fang;Tao Liu;Ronghua Wu;Liguo Liu;Chu He","doi":"10.1109/JSTARS.2025.3542952","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542952","url":null,"abstract":"With the swift advancement of deep learning, significant strides have been made in polarimetric synthetic aperture radar (PolSAR) image classification, particularly with the advent of diffusion models that allow for explicit class probability modeling. However, existing diffusion models have yet to fully leverage the rich polarimetric characteristics of PolSAR images. To address this, we propose the multilevel conditional diffusion (MCDiff) model for PolSAR image classification, incorporating three key strategies. First, a prior learning module is constructed to capture scattering characteristics across all three polarization basis parameter spaces, providing conditional guidance for the diffusion model. Second, a multiscale and multidimensional noise prediction module is designed to reduce the information loss when noisy labels and image features of different dimensions are fused to predict noise. Finally, a multilevel high-order statistical feature learning module is introduced to aid in the additive Gaussian noise prediction of noisy labels while mitigating the impact of PolSAR images' multiplicative speckle noise on the prediction. Experimental results on three benchmark datasets confirm MCDiff's ability to achieve high-performance explicit class probability modeling for PolSAR images among the compared methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6721-6737"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891635","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multimodal Semantic Segmentation Framework for Heterogeneous Optical and Complex SAR Data","authors":"Sining Xiao;Peijin Wang;Wenhui Diao;Kun Fu;Xian Sun","doi":"10.1109/JSTARS.2025.3542487","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542487","url":null,"abstract":"The advancement of remote sensing technology has led to a progressive enhancement in the resolution of remote sensing data, offering a multiperspective approach to Earth observation and facilitating a more comprehensive scene interpretation. As two most commonly utilized data sources in remote sensing, optical images, and synthetic aperture radar (SAR) data can provide complementary information, effectively compensating for the limitations inherent to a single modality. However, existing methods for using these two data sources face the following issues. First, insufficient utilization of the complete information provided by the source data. Second, inadequate consideration of the distinct characteristics of different modalities during feature extraction. Third, ignoring the misalignment between heterogeneous data, leading to large information loss. To tackle these challenges, we initially construct a benchmark dataset comprising complex-valued SAR data and optical images, named Multi-Complex-Seg. In order to fully mine the complete and valid information provided by both data sources, we construct a multimodal segmentation framework built on the theory of “subdomain extraction and cross-domain fusion,” in which we design a more suitable feature extractor for complex-valued SAR data, fully considering the unique geometric properties. In addition, a dynamic feature alignment module (DFAM) is proposed to further adjust the cross-modal features, and Cross-modal heterogeneous feature fusion module (CHFFM) first maps features into the same latent space to obtain better fused features. Both DFAM and CHFFM together reduce the huge semantic gap between modalities, thus facilitating the extraction of intramodal specificity and cross-modal complementarity. Extensive experiments on the proposed Multi-Complex-Seg confirm the effectiveness of our framework in comparison to other state-of-the-art multimodal segmentation approaches.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8083-8098"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}