Pub Date : 2026-01-19DOI: 10.1109/JSTARS.2026.3655359
Jianing Shao;Yanlei Du;Xiaofeng Yang;Longxiang Linghu;Jinsong Chong;Jian Yang
This study numerically investigates the spatial ergodicity of Doppler characteristics in polarimetric ocean radar scattering. The full Apel wave spectrum is employed to generate 2-D time-varying sea surfaces that involve all dominant large-scale gravity waves and small-scale capillary waves. By solving the radar scattering from time-varying ocean surfaces with various illumination sizes using the second-order small-slope approximation (SSA-2) model, the Doppler spectra, along with the Doppler shift and width, are thus computed and analyzed. The numerical simulations are conducted at L-band for three typical fully developed sea states. A Doppler shift error threshold is defined based on the accuracy requirements of sea surface current retrieval, and the spatial ergodicity of Doppler shift is evaluated quantitatively. Simulation results indicate that under co-polarization, the Doppler shift manifests spatial ergodicity when the sea surface size illuminated by radar is no less than one-quarter of the largest gravity wave wavelength at the corresponding sea state. For cross-polarization, the spatial ergodicity of the Doppler shift is significantly reduced and is observed only when the illumination size exceeds about one-half of the largest gravity wave wavelength. The results also indicate that wind direction has a limited effect on the spatial ergodicity of the Doppler shift.
{"title":"Spatial Ergodicity of Doppler Characteristics in Polarimetric Ocean Radar Scattering: A Numerical Study","authors":"Jianing Shao;Yanlei Du;Xiaofeng Yang;Longxiang Linghu;Jinsong Chong;Jian Yang","doi":"10.1109/JSTARS.2026.3655359","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3655359","url":null,"abstract":"This study numerically investigates the spatial ergodicity of Doppler characteristics in polarimetric ocean radar scattering. The full Apel wave spectrum is employed to generate 2-D time-varying sea surfaces that involve all dominant large-scale gravity waves and small-scale capillary waves. By solving the radar scattering from time-varying ocean surfaces with various illumination sizes using the second-order small-slope approximation (SSA-2) model, the Doppler spectra, along with the Doppler shift and width, are thus computed and analyzed. The numerical simulations are conducted at L-band for three typical fully developed sea states. A Doppler shift error threshold is defined based on the accuracy requirements of sea surface current retrieval, and the spatial ergodicity of Doppler shift is evaluated quantitatively. Simulation results indicate that under co-polarization, the Doppler shift manifests spatial ergodicity when the sea surface size illuminated by radar is no less than one-quarter of the largest gravity wave wavelength at the corresponding sea state. For cross-polarization, the spatial ergodicity of the Doppler shift is significantly reduced and is observed only when the illumination size exceeds about one-half of the largest gravity wave wavelength. The results also indicate that wind direction has a limited effect on the spatial ergodicity of the Doppler shift.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"5493-5506"},"PeriodicalIF":5.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358708","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175824","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}
Pub Date : 2026-01-19DOI: 10.1109/JSTARS.2026.3655550
Ch Muhammad Awais;Marco Reggiannini;Davide Moroni;Oktay Karakus
High-resolution imagery plays a critical role in improving the performance of visual recognition tasks such as classification, detection, and segmentation. In many domains, including remote sensing and surveillance, low-resolution images can limit the accuracy of automated analysis. To address this, superresolution techniques have been widely adopted to attempt to reconstruct high-resolution images from low-resolution inputs. Related traditional approaches focus solely on enhancing image quality based on pixel-level metrics, leaving the relationship between superresolved image fidelity and downstream classification performance largely underexplored. This raises a key question: Can integrating classification objectives directly into the superresolution process further improve classification accuracy? In this article, we try to respond to this question by investigating the relationship between superresolution and classification through the deployment of a specialized algorithmic strategy. We propose a novel methodology that increases the resolution of synthetic aperture radar imagery by optimizing loss functions that account for both image quality and classification performance. Our approach improves image quality, as measured by scientifically ascertained image quality indicators, while also enhancing classification accuracy.
{"title":"A Classification-Aware Superresolution Framework for Ship Targets in SAR Imagery","authors":"Ch Muhammad Awais;Marco Reggiannini;Davide Moroni;Oktay Karakus","doi":"10.1109/JSTARS.2026.3655550","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3655550","url":null,"abstract":"High-resolution imagery plays a critical role in improving the performance of visual recognition tasks such as classification, detection, and segmentation. In many domains, including remote sensing and surveillance, low-resolution images can limit the accuracy of automated analysis. To address this, superresolution techniques have been widely adopted to attempt to reconstruct high-resolution images from low-resolution inputs. Related traditional approaches focus solely on enhancing image quality based on pixel-level metrics, leaving the relationship between superresolved image fidelity and downstream classification performance largely underexplored. This raises a key question: Can integrating classification objectives directly into the superresolution process further improve classification accuracy? In this article, we try to respond to this question by investigating the relationship between superresolution and classification through the deployment of a specialized algorithmic strategy. We propose a novel methodology that increases the resolution of synthetic aperture radar imagery by optimizing loss functions that account for both image quality and classification performance. Our approach improves image quality, as measured by scientifically ascertained image quality indicators, while also enhancing classification accuracy.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"6614-6622"},"PeriodicalIF":5.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223541","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}
Pub Date : 2026-01-19DOI: 10.1109/JSTARS.2026.3655410
Meisam Shayegh Moradi;Kyle Foerster;Naima Kaabouch;Sheridan Parker;Aymane Ahajjam;Andrew Wilcox;Timothy J. Pasch
This paper introduces a high performance, distributed Artificial Intelligence (AI) based data processing architecture for Arctic multivariate geospatial feature prediction, designed to enhance heterogeneous weather modeling and support broader world scale simulations. The framework was validated on a 25 year Alaska air temperature dataset. The workflow partitions the dataset into spatio-temporally independent chunks, which are preprocessed and scored locally. Feature scores are then aggregated through a weighted voting mechanism, emphasizing predictors that remain consistently influential across diverse regions. This approach captures locally significant patterns while preserving their relevance in a broader global context, ensuring both regional detail and global coherence. Distributed data processing reduces computation time, improves model accuracy, and enhances generalization across heterogeneous Arctic landscapes. Eight feature selection techniques, including filter, wrapper, and embedded methods, were evaluated for predictor relevance and computational efficiency across distributed partitions. Four AI models spanning linear, non-linear, time series machine learning, and non-time series deep learning were trained on regionally diverse datasets to predict air temperature. Model performance was evaluated using MSE, sMAPE, KGE and MPE (%). Results indicate that distributed processing accelerates computation while achieving predictive performance equal to or better than serial methods. Techniques such as Kendall+DT and Pearson+MI demonstrate strong scalability with increasing dataset size and computational resources, highlighting their suitability for large scale Arctic datasets and integration into comprehensive global modeling frameworks. By capturing localized geophysical patterns and aggregating them globally, this approach enables more accurate and robust Arctic weather prediction and provides a foundation for broader world modeling applications.
{"title":"High Performance Distributed Data Processing Architecture for AI-Based Multivariate Arctic Geospatial Predictive Weather Modeling","authors":"Meisam Shayegh Moradi;Kyle Foerster;Naima Kaabouch;Sheridan Parker;Aymane Ahajjam;Andrew Wilcox;Timothy J. Pasch","doi":"10.1109/JSTARS.2026.3655410","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3655410","url":null,"abstract":"This paper introduces a high performance, distributed Artificial Intelligence (AI) based data processing architecture for Arctic multivariate geospatial feature prediction, designed to enhance heterogeneous weather modeling and support broader world scale simulations. The framework was validated on a 25 year Alaska air temperature dataset. The workflow partitions the dataset into spatio-temporally independent chunks, which are preprocessed and scored locally. Feature scores are then aggregated through a weighted voting mechanism, emphasizing predictors that remain consistently influential across diverse regions. This approach captures locally significant patterns while preserving their relevance in a broader global context, ensuring both regional detail and global coherence. Distributed data processing reduces computation time, improves model accuracy, and enhances generalization across heterogeneous Arctic landscapes. Eight feature selection techniques, including filter, wrapper, and embedded methods, were evaluated for predictor relevance and computational efficiency across distributed partitions. Four AI models spanning linear, non-linear, time series machine learning, and non-time series deep learning were trained on regionally diverse datasets to predict air temperature. Model performance was evaluated using MSE, sMAPE, KGE and MPE (%). Results indicate that distributed processing accelerates computation while achieving predictive performance equal to or better than serial methods. Techniques such as Kendall+DT and Pearson+MI demonstrate strong scalability with increasing dataset size and computational resources, highlighting their suitability for large scale Arctic datasets and integration into comprehensive global modeling frameworks. By capturing localized geophysical patterns and aggregating them globally, this approach enables more accurate and robust Arctic weather prediction and provides a foundation for broader world modeling applications.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"7627-7643"},"PeriodicalIF":5.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299579","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}
Pub Date : 2026-01-19DOI: 10.1109/JSTARS.2026.3655376
Haiwei Yu;Huapeng Li;Jian Lu;Tongtong Zhao;Baoqi Liu
Accurate crop yield estimation is essential for global food security, especially high-resolution mapping that supports field-scale management and detailed yield gap analysis. This study developed a hybrid yield estimation framework, named ensemble Kalman filter-random forest (EnKF-RF), which coupled data assimilation with a two-stage random forest approach. In this framework, Sentinel-2-derived leaf area index was first assimilated into the WOrld FOod Studies model using the EnKF algorithm. An RF-based metamodel (RF_SIM) was then trained to approximate the assimilation process, followed by a second RF model (RF_FIELD) that integrated land surface phenology, extreme-climate indicators, and limited ground observations to estimate crop yield. The proposed framework was applied to maize yield estimation in Jilin Province, China, during 2022–2024. The results showed that EnKF-RF achieved superior performance [R 2 = 0.476, root-mean-square error (RMSE) = 1565.87 kg/ha, and mean absolute error (MAE) = 1299.42 kg/ha] compared with a standalone random forest (R 2 = 0.394, RMSE = 1685.04 kg/ha, and MAE = 1428.69 kg/ha) and the scalable crop yield mapper approach. Furthermore, the implementation of the metamodel substantially enhanced the efficiency of the EnKF-RF framework, allowing annual maize yield estimation to be achieved within 18 min per 10 000 km2 in Jilin Province when utilizing Google Earth Engine. Water availability was identified as the primary driver of interannual yield variability, especially due to spring drought and the co-occurrence of water stress and waterlogging during July and August according to the SHapley Additive exPlanations. Generally, EnKF-RF provides a scalable and efficient solution for high-resolution maize yield mapping, particularly in data-scarce regions.
{"title":"Metamodel-Accelerated High-Resolution Maize Yield Mapping via Sentinel-2 Assimilation and Random Forest","authors":"Haiwei Yu;Huapeng Li;Jian Lu;Tongtong Zhao;Baoqi Liu","doi":"10.1109/JSTARS.2026.3655376","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3655376","url":null,"abstract":"Accurate crop yield estimation is essential for global food security, especially high-resolution mapping that supports field-scale management and detailed yield gap analysis. This study developed a hybrid yield estimation framework, named ensemble Kalman filter-random forest (EnKF-RF), which coupled data assimilation with a two-stage random forest approach. In this framework, Sentinel-2-derived leaf area index was first assimilated into the WOrld FOod Studies model using the EnKF algorithm. An RF-based metamodel (RF_SIM) was then trained to approximate the assimilation process, followed by a second RF model (RF_FIELD) that integrated land surface phenology, extreme-climate indicators, and limited ground observations to estimate crop yield. The proposed framework was applied to maize yield estimation in Jilin Province, China, during 2022–2024. The results showed that EnKF-RF achieved superior performance [<italic>R</i> <sup>2</sup> = 0.476, root-mean-square error (RMSE) = 1565.87 kg/ha, and mean absolute error (MAE) = 1299.42 kg/ha] compared with a standalone random forest (<italic>R</i> <sup>2</sup> = 0.394, RMSE = 1685.04 kg/ha, and MAE = 1428.69 kg/ha) and the scalable crop yield mapper approach. Furthermore, the implementation of the metamodel substantially enhanced the efficiency of the EnKF-RF framework, allowing annual maize yield estimation to be achieved within 18 min per 10 000 km<sup>2</sup> in Jilin Province when utilizing Google Earth Engine. Water availability was identified as the primary driver of interannual yield variability, especially due to spring drought and the co-occurrence of water stress and waterlogging during July and August according to the SHapley Additive exPlanations. Generally, EnKF-RF provides a scalable and efficient solution for high-resolution maize yield mapping, particularly in data-scarce regions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"6341-6358"},"PeriodicalIF":5.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223627","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}
Pub Date : 2026-01-19DOI: 10.1109/JSTARS.2026.3655350
Wenjing Li;Libin Du;Xinglei Zhao
Accurate water-land classification is fundamental for topographic mapping and coastal zone monitoring based on airborne LiDAR bathymetry (ALB). However, due to the limited information content and feature ambiguity of one-dimensional (1-D) waveform signals, accurate classification from single-wavelength ALB data remains challenging. To address this issue, a dual-branch multimodal fusion network (CRMF-Net) is proposed to improve both classification accuracy and robustness. The proposed network consists of a convolutional neural network (CNN) branch and a convolutional block attention module optimized residual neural network branch, which are designed to capture complementary temporal and spatial features, respectively. The 1-D green waveform is converted into a 2-D time-frequency representation through the continuous wavelet transform, thereby increasing the dimensions and quantity of waveform features. By jointly exploiting complementary information from waveform signals and their corresponding time–frequency representations, the proposed method enables more effective feature representation without relying on extensive handcrafted analysis. Experiments conducted on CZMIL datasets from Qinshan Island demonstrate that CRMF-Net achieves an overall accuracy of 97.33% with a kappa coefficient of 0.9168, outperforming traditional methods, such as fuzzy C-means, support vector machine, and the one-dimensional convolutional neural network approach. These results indicate that the proposed method provides a promising solution for fully automated processing of single-wavelength ALB data.
{"title":"CRMF-Net: A Multimodal Fusion Network for Water–Land Classification From Single-Wavelength Bathymetric LiDAR","authors":"Wenjing Li;Libin Du;Xinglei Zhao","doi":"10.1109/JSTARS.2026.3655350","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3655350","url":null,"abstract":"Accurate water-land classification is fundamental for topographic mapping and coastal zone monitoring based on airborne LiDAR bathymetry (ALB). However, due to the limited information content and feature ambiguity of one-dimensional (1-D) waveform signals, accurate classification from single-wavelength ALB data remains challenging. To address this issue, a dual-branch multimodal fusion network (CRMF-Net) is proposed to improve both classification accuracy and robustness. The proposed network consists of a convolutional neural network (CNN) branch and a convolutional block attention module optimized residual neural network branch, which are designed to capture complementary temporal and spatial features, respectively. The 1-D green waveform is converted into a 2-D time-frequency representation through the continuous wavelet transform, thereby increasing the dimensions and quantity of waveform features. By jointly exploiting complementary information from waveform signals and their corresponding time–frequency representations, the proposed method enables more effective feature representation without relying on extensive handcrafted analysis. Experiments conducted on CZMIL datasets from Qinshan Island demonstrate that CRMF-Net achieves an overall accuracy of 97.33% with a kappa coefficient of 0.9168, outperforming traditional methods, such as fuzzy C-means, support vector machine, and the one-dimensional convolutional neural network approach. These results indicate that the proposed method provides a promising solution for fully automated processing of single-wavelength ALB data.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"5804-5813"},"PeriodicalIF":5.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175694","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}
Pub Date : 2026-01-19DOI: 10.1109/JSTARS.2026.3655144
Marta Alonso Tubía;Miguel Baena Botana;An Vo Quang;Ana Burgin;Oliva Garcia Cantú-Ros
Dynamic population mapping has become crucial for capturing real-time human movement and behavior, beyond traditional population mapping relying on census data. Differentiating indoor and outdoor activity enhances accuracy for smart city planning, emergency response, public health, or emerging technologies like Innovative Air Mobility, where pedestrian data informs safer, less disruptive flight planning. Data passively collected from mobile networks have proven to be highly effective in accurately capturing population presence and mobility patterns. By enhancing this rich data source with GPS data for spatial accuracy and validating the results with satellite imagery of detected pedestrians, we provide a procedure for indoor and outdoor population detection. The results show agreement between both methodologies. Despite some limitations related to GPS data biases and pedestrian detection issues caused by urban furniture and shadows, the procedure demonstrates strong potential to capture people’s movements, which could ultimately enable near real-time monitoring of population presence on the streets.
{"title":"Toward Outdoor Population Presence Monitoring With Mobile Network Data and Satellite Imagery","authors":"Marta Alonso Tubía;Miguel Baena Botana;An Vo Quang;Ana Burgin;Oliva Garcia Cantú-Ros","doi":"10.1109/JSTARS.2026.3655144","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3655144","url":null,"abstract":"Dynamic population mapping has become crucial for capturing real-time human movement and behavior, beyond traditional population mapping relying on census data. Differentiating indoor and outdoor activity enhances accuracy for smart city planning, emergency response, public health, or emerging technologies like Innovative Air Mobility, where pedestrian data informs safer, less disruptive flight planning. Data passively collected from mobile networks have proven to be highly effective in accurately capturing population presence and mobility patterns. By enhancing this rich data source with GPS data for spatial accuracy and validating the results with satellite imagery of detected pedestrians, we provide a procedure for indoor and outdoor population detection. The results show agreement between both methodologies. Despite some limitations related to GPS data biases and pedestrian detection issues caused by urban furniture and shadows, the procedure demonstrates strong potential to capture people’s movements, which could ultimately enable near real-time monitoring of population presence on the streets.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"5834-5852"},"PeriodicalIF":5.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358662","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175807","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}
Pub Date : 2026-01-16DOI: 10.1109/JSTARS.2026.3654602
Ming Tong;Shenghua Fan;Jiu Jiang;Hezhi Sun;Jisan Yang;Chu He
Recently, detectors based on deep learning have boosted the state-of-the-art of application on ship detection in synthetic aperture radar (SAR) images. However, constructing discriminative feature from scattering of background and distinguishing contour of ship precisely still present challenging subject to the inherent scattering mechanism of SAR. In this article, a dual-branch detection framework with perception of scattering characteristic and geometric contour is introduced to deal with the problem. First, a scattering characteristic perception branch is proposed to fit the scattering distribution of SAR ship through conditional diffusion model, which introduces learnable scattering feature. Second, a convex contour perception branch is designed as two-stage coarse-to-fine pipeline to delimit the irregular boundary of ship by learning scattering key points. Finally, a cross-token integration module following Bayesian framework is introduced to couple features of scattering and texture adaptively to learn construction of discriminative feature. Furthermore, comprehensive experiments on three authoritative SAR datasets for oriented ship detection demonstrate the effectiveness of proposed method.
{"title":"Dual-Perception Detector for Ship Detection in SAR Images","authors":"Ming Tong;Shenghua Fan;Jiu Jiang;Hezhi Sun;Jisan Yang;Chu He","doi":"10.1109/JSTARS.2026.3654602","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3654602","url":null,"abstract":"Recently, detectors based on deep learning have boosted the state-of-the-art of application on ship detection in synthetic aperture radar (SAR) images. However, constructing discriminative feature from scattering of background and distinguishing contour of ship precisely still present challenging subject to the inherent scattering mechanism of SAR. In this article, a dual-branch detection framework with perception of scattering characteristic and geometric contour is introduced to deal with the problem. First, a scattering characteristic perception branch is proposed to fit the scattering distribution of SAR ship through conditional diffusion model, which introduces learnable scattering feature. Second, a convex contour perception branch is designed as two-stage coarse-to-fine pipeline to delimit the irregular boundary of ship by learning scattering key points. Finally, a cross-token integration module following Bayesian framework is introduced to couple features of scattering and texture adaptively to learn construction of discriminative feature. Furthermore, comprehensive experiments on three authoritative SAR datasets for oriented ship detection demonstrate the effectiveness of proposed method.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"4790-4808"},"PeriodicalIF":5.3,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11355870","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082030","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}
Pub Date : 2026-01-16DOI: 10.1109/JSTARS.2026.3655033
Yixin Zhu;Zhimin Sha;Pengzhi Wei;Shirong Ye;Pengfei Xia;Fangxin Hu
Tropospheric delay, for which water vapor is a major cause, is a significant source of error in the global navigation satellite system. This article presents the gray figure-based zenith tropospheric delay prediction (GFZTD) model, which is built on convolutional long short-term memory networks and self-attention mechanisms. The model converts 3-D zenith tropospheric delay (ZTD) grid products into multilayer 2-D grayscale images for predictive analysis. Utilizing the global forecast system (GFS) and ERA5 data from southeastern China and its adjacent seas in 2023, the GFZTD model is trained through seasonal slicing and stratification by altitude. This approach generates high spatiotemporal resolution ZTD 3-D grid products in near real time. To evaluate the grid prediction results, ERA5 is used as the truth, with an overall root-mean-square error (RMSE) of 1.35 cm, representing improvements of 26.5% and 71.0% over ZTD derived from GFS and global pressure and temperature 3 (GPT3), respectively. The model also successfully mitigates regional extreme prediction errors in complex terrain environments for GFS. In addition, when using Vienna mapping function 3 postprocessing products to assess ZTD prediction values at various stations, the GFZTD model shows an average RMSE of 1.49 cm. This result indicates the improvements of 13.1% and 69.4% compared with GFS and GPT3, respectively, underscoring the model's applicability at the station scale.
{"title":"GFZTD: A Multimodal Fusion-Driven 3-D Tropospheric Delay Prediction Model Coupling Self-Attention and ConvLSTM","authors":"Yixin Zhu;Zhimin Sha;Pengzhi Wei;Shirong Ye;Pengfei Xia;Fangxin Hu","doi":"10.1109/JSTARS.2026.3655033","DOIUrl":"https://doi.org/10.1109/JSTARS.2026.3655033","url":null,"abstract":"Tropospheric delay, for which water vapor is a major cause, is a significant source of error in the global navigation satellite system. This article presents the gray figure-based zenith tropospheric delay prediction (GFZTD) model, which is built on convolutional long short-term memory networks and self-attention mechanisms. The model converts 3-D zenith tropospheric delay (ZTD) grid products into multilayer 2-D grayscale images for predictive analysis. Utilizing the global forecast system (GFS) and ERA5 data from southeastern China and its adjacent seas in 2023, the GFZTD model is trained through seasonal slicing and stratification by altitude. This approach generates high spatiotemporal resolution ZTD 3-D grid products in near real time. To evaluate the grid prediction results, ERA5 is used as the truth, with an overall root-mean-square error (RMSE) of 1.35 cm, representing improvements of 26.5% and 71.0% over ZTD derived from GFS and global pressure and temperature 3 (GPT3), respectively. The model also successfully mitigates regional extreme prediction errors in complex terrain environments for GFS. In addition, when using Vienna mapping function 3 postprocessing products to assess ZTD prediction values at various stations, the GFZTD model shows an average RMSE of 1.49 cm. This result indicates the improvements of 13.1% and 69.4% compared with GFS and GPT3, respectively, underscoring the model's applicability at the station scale.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"19 ","pages":"6375-6388"},"PeriodicalIF":5.3,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11355947","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223650","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}
Pub Date : 2026-01-15DOI: 10.1109/JSTARS.2026.3654346
Jianshang Liao;Liguo Wang
Hyperspectral image (HSI) classification faces critical challenges in effectively modeling long-range dependencies while maintaining computational efficiency and synergistically exploiting spatial-spectral information. Convolutional neural networks (CNNs) are constrained by local receptive fields, transformers suffer from quadratic computational complexity, and existing state space model (SSM)-based methods lack sophisticated cross-domain interaction mechanisms. This article proposes Spatial-Spectral Attentive Mamba (SSA-Mamba), a novel classification approach addressing these limitations through three synergistic innovations. First, a dual-branch independent modeling strategy allocates separate parameter spaces for spatial and spectral feature extraction via parallel SSMs, preventing feature coupling while enabling domain-specific learning. Second, an asymmetric cross-domain attention mechanism allows spatial features to actively query spectral information through multihead attention, establishing adaptive fusion via gating mechanisms and channel attention. Third, a multiscale residual architecture operating at module-internal, block-internal, and global pathway levels achieves hierarchical feature fusion while maintaining numerical stability through exponential parameterization. The recursive computation mechanism of SSMs enables each position to aggregate global historical information through compact hidden states, achieving O(L) linear complexity compared to transformers’ O(L2) quadratic complexity. Extensive experiments on three benchmark datasets—Houston2013, WHU-Hi-HongHu, and XiongAn—validate the effectiveness of these innovations. SSA-Mamba achieves overall accuracies of 93.98%, 93.58%, and 96.06%, surpassing state-of-the-art approaches by 1.27%, 0.25%, and 1.27%, respectively. The dual-branch design enables effective discrimination of spectrally similar categories, improving Brassica variety classification by 19.21–23.33 percentage points over coupled-feature approaches. The cross-domain attention mechanism enhances urban land cover classification, with Commercial and Highway categories improving by 1.74% and 15.66%. On the large-scale XiongAn dataset (5.92 million pixels), SSA-Mamba demonstrates exceptional scalability with peak GPU memory of only 317.89 MB and per-sample inference time of 0.646 ms, providing an efficient solution for real-time HSI processing. The source code for SSA-Mamba will be made publicly available online.
高光谱图像（HSI）分类面临着在保持计算效率和协同利用空间光谱信息的同时有效建模远程依赖关系的关键挑战。卷积神经网络（cnn）受局部感受场的限制，变压器的计算复杂度为二次型，现有的基于状态空间模型（SSM）的方法缺乏复杂的跨域交互机制。本文提出了空间光谱关注曼巴（SSA-Mamba），这是一种新的分类方法，通过三个协同创新来解决这些限制。首先，双分支独立建模策略通过并行ssm为空间和光谱特征提取分配单独的参数空间，在实现特定领域学习的同时防止特征耦合。其次，非对称跨域注意机制允许空间特征通过多头注意主动查询光谱信息，通过门控机制和通道注意建立自适应融合；第三，在模块内部、块内部和全局路径水平上运行的多尺度残差架构实现了分层特征融合，同时通过指数参数化保持了数值稳定性。ssm的递归计算机制使每个位置能够通过紧凑的隐藏状态聚合全局历史信息，与变压器的O（L2）二次复杂度相比，实现了O(L)线性复杂度。在休斯顿2013、whu - hi -洪湖和雄安三个基准数据集上进行的大量实验验证了这些创新的有效性。SSA-Mamba的总体准确率分别为93.98%、93.58%和96.06%，比目前最先进的方法分别高出1.27%、0.25%和1.27%。双分支设计能够有效识别光谱相似的品类，比耦合特征方法提高了19.21-23.33个百分点。跨域关注机制增强了城市土地覆盖分类，商业类和公路类分别提高了1.74%和15.66%。在大规模雄安数据集（592万像素）上，SSA-Mamba显示出卓越的可扩展性，峰值GPU内存仅为317.89 MB，每样本推理时间为0.646 ms，为实时HSI处理提供了有效的解决方案。SSA-Mamba的源代码将在网上公开。
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Pub Date : 2026-01-14DOI: 10.1109/JSTARS.2026.3654241
Yuan Li;Tianzhu Zhang;Ziyi Xiong;Junying Lv;Yinning Pang
Detecting three-dimensional (3-D) windows is vital for creating semantic building models with high level of detail, furnishing smart city and digital twin programs. Existing studies on window extraction using street imagery or laser scanning data often rely on limited types of features, resulting in compromised accuracy and completeness due to shadows and geometric decorations caused by curtains, balconies, plants, and other objects. To enhance the effectiveness and robustness of building window extraction in 3-D, this article proposes an automatic method that leverages synergistic information from multiview-stereo (MVS) point clouds, through an adaptive divide-and-combine pipeline. Color information inherited from the imagery serves as a main clue to acquire the point clouds of individual building façades that may be coplanar and connected. The geometric information associated with normal vectors is then combined with color, to adaptively divide individual building façade into an irregular grid that conforms to the window edges. Subsequently, HSV color and depth distances within each grid cell are computed, and the grid cells are encoded to quantify the global arrangement features of windows. Finally, the multitype features are fused in an integer programming model, by solving which the optimal combination of grid cells corresponding to windows is obtained. Benefitting from the informative MVS point clouds and the fusion of multitype features, our method is able to directly produce 3-D models with high regularity for buildings with different appearances. Experimental results demonstrate that the proposed method is effective in 3-D window extraction while overcoming variations in façade appearances caused by foreign objects and missing data, with a high point-wise precision of 92.7%, recall of 77.09%, IoU of 71.95%, and F1-score of 83.42%. The results also exhibit a high level of integrity, with the accuracy of correctly extracted windows reaching 89.81%. In the future, we will focus on the development of a more universal façade dividing method to deal with even more complicated windows.
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