Remote sensing image segmentation is particularly difficult due to the coexistence of large-scale variations and fine-grained structures in very high-resolution imagery. Conventional CNN-based or transformer-based networks often struggle to capture global context while preserving boundary details, leading to degraded performance on small or thin objects. To address these challenges, we propose a self-prompt calibration network based on segment anything model 2 (SC-SAM). The SC-SAM achieves self-prompt by feeding mask prompts from a lightweight decoder into frozen prompt encoder. Output calibration is achieved through the proposed cross-probability guided calibration (CPGC) module, which employs cross-probability uncertainty as complementary guidance to refine final predictions via self-prompted outputs. Furthermore, to better preserve contextual and structural information across multiple scales, a scale-decoupled kernel mixture (SDKM) module is designed. Experimental results on the ISPRS Vaihingen and Potsdam dataset demonstrate that the proposed approach surpasses the state-of-the-art methods by 1.02% and 1.34% in mIoU, highlighting its effectiveness. This study provides new insights into adapting SAM for domain-specific remote sensing segmentation tasks.
{"title":"A Self-Prompt Calibration Network Based on Segment Anything Model 2 for High-Resolution Remote Sensing Image Segmentation","authors":"Yizhou Lan;Daoyuan Zheng;Xinge Zhao;Ke Shang;Feizhou Zhang","doi":"10.1109/LGRS.2025.3636177","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3636177","url":null,"abstract":"Remote sensing image segmentation is particularly difficult due to the coexistence of large-scale variations and fine-grained structures in very high-resolution imagery. Conventional CNN-based or transformer-based networks often struggle to capture global context while preserving boundary details, leading to degraded performance on small or thin objects. To address these challenges, we propose a self-prompt calibration network based on segment anything model 2 (SC-SAM). The SC-SAM achieves self-prompt by feeding mask prompts from a lightweight decoder into frozen prompt encoder. Output calibration is achieved through the proposed cross-probability guided calibration (CPGC) module, which employs cross-probability uncertainty as complementary guidance to refine final predictions via self-prompted outputs. Furthermore, to better preserve contextual and structural information across multiple scales, a scale-decoupled kernel mixture (SDKM) module is designed. Experimental results on the ISPRS Vaihingen and Potsdam dataset demonstrate that the proposed approach surpasses the state-of-the-art methods by 1.02% and 1.34% in mIoU, highlighting its effectiveness. This study provides new insights into adapting SAM for domain-specific remote sensing segmentation tasks.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To be effective, ecosystem and habitat conservation must not only look at past losses but also understand the effects of current and future decisions on landscapes. Here, we present a transformative, user-driven land cover change prediction tool designed to aid land planners in strategic decision-making for conservation and habitat protection. Within an integrated map-based prediction pipeline, the tool uses machine learning (ML) and deep learning (DL) models to classify satellite images and make predictions of near-term land cover changes. The tool facilitates user interaction with a cloud-hosted ML model, making it accessible to nontechnical users for generating map-based predictions using big data. The tool’s key strength lies in its dynamic variable adjustment feature, empowering users to tailor scenarios related to potential future development planning. Through the integration of cloud-hosted ML and DL models with a user-centric interface, the tool has the potential to allow stakeholders and land planners to make informed decisions, actively minimizing habitat destruction and aligning with broader conservation objectives. We tested our approach in the context of central Texas, USA to evaluate its effectiveness in diverse conservation scenarios, with an average overall accuracy of 88% for the land cover class maps over four years and over 72% for the five-year land cover change prediction. While our approach has the potential to improve land management and planning for conservation, we also acknowledge the importance of rigorous model validation and ongoing refinement and highlight the need for technological advancement to be developed with strong stakeholder engagement.
{"title":"User-Driven Land Cover Change Prediction Map Tool for Land Conservation Planning","authors":"Pui-Yu Ling;Laura Nunes;Jonathan Srinivasan;Nasir Popalzay;Palmer Wilson;Jameson Quisenberry;Alex Borowicz","doi":"10.1109/LGRS.2025.3636286","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3636286","url":null,"abstract":"To be effective, ecosystem and habitat conservation must not only look at past losses but also understand the effects of current and future decisions on landscapes. Here, we present a transformative, user-driven land cover change prediction tool designed to aid land planners in strategic decision-making for conservation and habitat protection. Within an integrated map-based prediction pipeline, the tool uses machine learning (ML) and deep learning (DL) models to classify satellite images and make predictions of near-term land cover changes. The tool facilitates user interaction with a cloud-hosted ML model, making it accessible to nontechnical users for generating map-based predictions using big data. The tool’s key strength lies in its dynamic variable adjustment feature, empowering users to tailor scenarios related to potential future development planning. Through the integration of cloud-hosted ML and DL models with a user-centric interface, the tool has the potential to allow stakeholders and land planners to make informed decisions, actively minimizing habitat destruction and aligning with broader conservation objectives. We tested our approach in the context of central Texas, USA to evaluate its effectiveness in diverse conservation scenarios, with an average overall accuracy of 88% for the land cover class maps over four years and over 72% for the five-year land cover change prediction. While our approach has the potential to improve land management and planning for conservation, we also acknowledge the importance of rigorous model validation and ongoing refinement and highlight the need for technological advancement to be developed with strong stakeholder engagement.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11265796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-24DOI: 10.1109/LGRS.2025.3636165
Nguyen Anh Tu;Nursultan Makhanov;Kenzhebek Taniyev;Ton Duc Do
Aerial video captioning (VC) facilitates the automatic interpretation of dynamic scenes in remote sensing (RS), supporting critical applications, such as disaster response, traffic monitoring, and environmental surveillance. However, challenges, such as extreme angles and continuous camera motion, require adaptive modeling of complex temporal relationships. To tackle these challenges, we leverage an image-language model as the vision encoder and introduce a temporal adaptation module that combines convolution with self-attention layers to both capture local semantics across neighboring frames and model global temporal dependencies. This design allows our model to exploit the multimodal knowledge of the vision encoder while effectively reasoning over the spatiotemporal dynamics. In addition, privacy concerns often restrict access to annotated aerial datasets, posing further challenges for model training. To address this, we develop a federated learning (FL) framework that enables collaborative model training across decentralized clients. Within this framework, we establish a unified benchmark for systematic comparison of temporal adapters, text decoders, and FL strategies, hence filling a gap in the existing literature. Extensive experiments validate the robustness of our approach and its potential for advancing aerial VC.
{"title":"Federated Aerial Video Captioning With Effective Temporal Adaptation","authors":"Nguyen Anh Tu;Nursultan Makhanov;Kenzhebek Taniyev;Ton Duc Do","doi":"10.1109/LGRS.2025.3636165","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3636165","url":null,"abstract":"Aerial video captioning (VC) facilitates the automatic interpretation of dynamic scenes in remote sensing (RS), supporting critical applications, such as disaster response, traffic monitoring, and environmental surveillance. However, challenges, such as extreme angles and continuous camera motion, require adaptive modeling of complex temporal relationships. To tackle these challenges, we leverage an image-language model as the vision encoder and introduce a temporal adaptation module that combines convolution with self-attention layers to both capture local semantics across neighboring frames and model global temporal dependencies. This design allows our model to exploit the multimodal knowledge of the vision encoder while effectively reasoning over the spatiotemporal dynamics. In addition, privacy concerns often restrict access to annotated aerial datasets, posing further challenges for model training. To address this, we develop a federated learning (FL) framework that enables collaborative model training across decentralized clients. Within this framework, we establish a unified benchmark for systematic comparison of temporal adapters, text decoders, and FL strategies, hence filling a gap in the existing literature. Extensive experiments validate the robustness of our approach and its potential for advancing aerial VC.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-24DOI: 10.1109/LGRS.2025.3636236
Qi Zeng;Wanchun Zhang;Jie Cheng
This study develops an integrated framework for all-sky surface longwave downward radiation (SLDR) estimate for the medium resolution spectral imager-II (MERSI-II) onboard the Fengyun-3D (FY-3D) satellite. The framework comprises a hybrid method for the clear-sky SLDR estimate and a cloud base temperature (CBT)-based single-layer cloud model (SLCM) for the cloudy-sky SLDR estimate. In situ validation indicates that the hybrid method yields a bias/RMSE of −0.78/21.70 W/m2, whereas the SLCM achieves a bias/RMSE of 5.79/23.61 W/m2. The bias/RMSE of the all-sky SLDR is 3.37/22.93 W/m2. The estimated all-sky instantaneous SLDR was combined with ERA5 temporal information to derive daily SLDR using a bias-corrected sinusoidal integration method, yielding a bias of 0.04 W/m2 and an RMSE of 16.77 W/m2. These results demonstrate the robustness of the proposed framework and its substantial potential in generating both instantaneous and daily SLDR products at 1 km spatial resolution.
{"title":"An Integrated Framework for Estimating the All-Sky Surface Downward Longwave Radiation From FY-3D/MERSI-II Imagery","authors":"Qi Zeng;Wanchun Zhang;Jie Cheng","doi":"10.1109/LGRS.2025.3636236","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3636236","url":null,"abstract":"This study develops an integrated framework for all-sky surface longwave downward radiation (SLDR) estimate for the medium resolution spectral imager-II (MERSI-II) onboard the Fengyun-3D (FY-3D) satellite. The framework comprises a hybrid method for the clear-sky SLDR estimate and a cloud base temperature (CBT)-based single-layer cloud model (SLCM) for the cloudy-sky SLDR estimate. In situ validation indicates that the hybrid method yields a bias/RMSE of −0.78/21.70 W/m2, whereas the SLCM achieves a bias/RMSE of 5.79/23.61 W/m2. The bias/RMSE of the all-sky SLDR is 3.37/22.93 W/m2. The estimated all-sky instantaneous SLDR was combined with ERA5 temporal information to derive daily SLDR using a bias-corrected sinusoidal integration method, yielding a bias of 0.04 W/m2 and an RMSE of 16.77 W/m2. These results demonstrate the robustness of the proposed framework and its substantial potential in generating both instantaneous and daily SLDR products at 1 km spatial resolution.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1109/LGRS.2025.3635413
Brent Martin;Norman W. H. Mason;James D. Shepherd;Jan Schindler
The New Zealand Land Use Carbon Analysis System Land Use Map (LUCAS LUM) is a series of land use layers that map land use classes, including both exotic and native forest, dating back to 1990 and updated every four years since 2008. This map is a rich resource, but the significant effort required to update it means errors may creep in without detection. We trialed whether a deep learning model could be trained on this imperfect data. We found the model predicts exotic forestry nationally to a higher level of accuracy than previously achieved. The resulting layer was used to detect and correct missed exotic forest plantations in the current LUCAS LUM. We also demonstrate that the exotic forestry prediction is sufficiently sensitive to detect wilding conifer infestations and estimate infestation density. Our results highlight the effectiveness of weakly supervised learning, enabling accurate and scalable national land use and land cover mapping while drastically reducing manual labeling efforts.
{"title":"Improving New Zealand’s Vegetation Mapping Using Weakly Supervised Learning","authors":"Brent Martin;Norman W. H. Mason;James D. Shepherd;Jan Schindler","doi":"10.1109/LGRS.2025.3635413","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3635413","url":null,"abstract":"The New Zealand Land Use Carbon Analysis System Land Use Map (LUCAS LUM) is a series of land use layers that map land use classes, including both exotic and native forest, dating back to 1990 and updated every four years since 2008. This map is a rich resource, but the significant effort required to update it means errors may creep in without detection. We trialed whether a deep learning model could be trained on this imperfect data. We found the model predicts exotic forestry nationally to a higher level of accuracy than previously achieved. The resulting layer was used to detect and correct missed exotic forest plantations in the current LUCAS LUM. We also demonstrate that the exotic forestry prediction is sufficiently sensitive to detect wilding conifer infestations and estimate infestation density. Our results highlight the effectiveness of weakly supervised learning, enabling accurate and scalable national land use and land cover mapping while drastically reducing manual labeling efforts.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Forest tree species classification has great significance for sustainable development of forest resource. Multisource remote sensing data provide abundant temporal, spatial, and spectral information for tree species classification. However, there lacks tree species classification methods, which comprehensively capture and fuse spatio–temporal–spectral information. Therefore, a tree species classification method based on deep ensemble learning of multisource spatio–temporal–spectral remote sensing data is proposed. First, multitemporal, high-resolution, and hyperspectral data are utilized for training temporal, spatial, and spectral deep networks. Furtherly, deep ensemble learning is developed for the fusion of spatio–temporal–spectral network outputs, where weighted fusion is implemented via dynamic weight optimization based on the spatio–temporal–spatial features. Experimental results indicate that the importance of temporal features is higher than that of spatial information, and spectral networks perform best among all network structures. After the spatio–temporal–spectral ensemble learning, the performance of tree species classification is further improved, and the overall accuracy (OA) of the proposed method reaches above 90%. The proposed algorithm realizes precise and fine-scale tree species classification and provides technique support for the monitoring and conservation of forest resource.
{"title":"Forest Tree Species Classification Based on Deep Ensemble Learning by Fusing High-Resolution, Multitemporal, and Hyperspectral Multisource Remote Sensing Data","authors":"Dengli Yu;Lilin Tu;Ziqing Wei;Fuyao Zhu;Chengjun Yu;Denghong Wang;Jiayi Li;Xin Huang","doi":"10.1109/LGRS.2025.3634553","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3634553","url":null,"abstract":"Forest tree species classification has great significance for sustainable development of forest resource. Multisource remote sensing data provide abundant temporal, spatial, and spectral information for tree species classification. However, there lacks tree species classification methods, which comprehensively capture and fuse spatio–temporal–spectral information. Therefore, a tree species classification method based on deep ensemble learning of multisource spatio–temporal–spectral remote sensing data is proposed. First, multitemporal, high-resolution, and hyperspectral data are utilized for training temporal, spatial, and spectral deep networks. Furtherly, deep ensemble learning is developed for the fusion of spatio–temporal–spectral network outputs, where weighted fusion is implemented via dynamic weight optimization based on the spatio–temporal–spatial features. Experimental results indicate that the importance of temporal features is higher than that of spatial information, and spectral networks perform best among all network structures. After the spatio–temporal–spectral ensemble learning, the performance of tree species classification is further improved, and the overall accuracy (OA) of the proposed method reaches above 90%. The proposed algorithm realizes precise and fine-scale tree species classification and provides technique support for the monitoring and conservation of forest resource.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1109/LGRS.2025.3634593
HengYu Li;Bo Huang;JianYong Lv
Driven by the increasing demand for intelligent Earth observation and large-scale scene understanding, remote sensing object detection has gained significant academic and practical importance. Despite notable progress in feature extraction and computational efficiency, many recent approaches still struggle to effectively handle issues such as detecting objects at multiple scales and preserving small targets. In this letter, an efficient remote sensing object detector called multiscale and feature-preserving YOLO with gated attention (YOLO-MFG) is proposed to address these challenges. First, a multiscale group shuffle attention (MGSA) module is introduced to adaptively aggregate multiscale spatial features, improving the model’s sensitivity to objects of diverse sizes. Second, the use of feature-preserving downsampling (FPD) enhances the downsampling process by introducing a triple-branch fusion mechanism that mitigates aliasing while jointly preserving semantics, saliency, and geometry. Finally, gated enhanced attention (GEA) is integrated to capture long-range dependencies and contextual cues crucial for remote sensing scenarios. The experimental results demonstrate that the proposed YOLO-MFG achieves a 2.9% improvement in mean average precision at an intersection over union (IoU) threshold of 0.5 (mAP50) on the optical remote sensing dataset SIMD compared with YOLO11. In addition, the mAP50 of detection results is improved by 1.4% and 4.2% on the DIOR and NWPU VHR-10 datasets, respectively.
{"title":"YOLO-MFG: Multiscale and Feature-Preserving YOLO With Gated Attention for Remote Sensing Object Detection","authors":"HengYu Li;Bo Huang;JianYong Lv","doi":"10.1109/LGRS.2025.3634593","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3634593","url":null,"abstract":"Driven by the increasing demand for intelligent Earth observation and large-scale scene understanding, remote sensing object detection has gained significant academic and practical importance. Despite notable progress in feature extraction and computational efficiency, many recent approaches still struggle to effectively handle issues such as detecting objects at multiple scales and preserving small targets. In this letter, an efficient remote sensing object detector called multiscale and feature-preserving YOLO with gated attention (YOLO-MFG) is proposed to address these challenges. First, a multiscale group shuffle attention (MGSA) module is introduced to adaptively aggregate multiscale spatial features, improving the model’s sensitivity to objects of diverse sizes. Second, the use of feature-preserving downsampling (FPD) enhances the downsampling process by introducing a triple-branch fusion mechanism that mitigates aliasing while jointly preserving semantics, saliency, and geometry. Finally, gated enhanced attention (GEA) is integrated to capture long-range dependencies and contextual cues crucial for remote sensing scenarios. The experimental results demonstrate that the proposed YOLO-MFG achieves a 2.9% improvement in mean average precision at an intersection over union (IoU) threshold of 0.5 (mAP50) on the optical remote sensing dataset SIMD compared with YOLO11. In addition, the mAP50 of detection results is improved by 1.4% and 4.2% on the DIOR and NWPU VHR-10 datasets, respectively.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-19DOI: 10.1109/LGRS.2025.3634759
Shengyi Wang;Xuehua Chen;Cong Wang;Junjie Liu;Xin Luo
High-resolution time–frequency analysis is crucial for seismic interpretation. Conventional sparse time–frequency transforms, such as the sparse generalized S transform (SGST), are not adaptive to the intrinsic characteristics of the signal. To address this limitation, we propose a sparse adaptive generalized S transform (SAGST). This method incorporates the signal amplitude spectrum into the Gaussian window function, allowing the window to adapt dynamically to the signal characteristics. This adaptive mechanism enables the construction of wavelet bases that are better matched to the signal. We apply the SAGST to the time–frequency analysis of both synthetic signal and field seismic data. The synthetic signal test shows that the SAGST achieves higher energy concentration, superior computational efficiency, and enhanced weak signal extraction compared with the sparse adaptive S transform (SAST) and SGST. A field example demonstrates that the SAGST can be used to indicate low-frequency shadow associated with hydrocarbon reservoirs.
{"title":"The Sparse Adaptive Generalized S Transform","authors":"Shengyi Wang;Xuehua Chen;Cong Wang;Junjie Liu;Xin Luo","doi":"10.1109/LGRS.2025.3634759","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3634759","url":null,"abstract":"High-resolution time–frequency analysis is crucial for seismic interpretation. Conventional sparse time–frequency transforms, such as the sparse generalized S transform (SGST), are not adaptive to the intrinsic characteristics of the signal. To address this limitation, we propose a sparse adaptive generalized S transform (SAGST). This method incorporates the signal amplitude spectrum into the Gaussian window function, allowing the window to adapt dynamically to the signal characteristics. This adaptive mechanism enables the construction of wavelet bases that are better matched to the signal. We apply the SAGST to the time–frequency analysis of both synthetic signal and field seismic data. The synthetic signal test shows that the SAGST achieves higher energy concentration, superior computational efficiency, and enhanced weak signal extraction compared with the sparse adaptive S transform (SAST) and SGST. A field example demonstrates that the SAGST can be used to indicate low-frequency shadow associated with hydrocarbon reservoirs.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1109/LGRS.2025.3634350
Wenqiang Ding;Changying Ma;Xintong Dong;Xuan Li
The heterogeneity of subsurface media induces multipath scattering and dielectric loss in ground penetrating radar (GPR) signal propagation, which results in wavefront distortion and signal attenuation. These effects degrade B-scan profiles by blurring target signatures, hindering automated feature extraction, and reducing the clarity of regions of interest (ROI). To address these issues, we propose the adaptive region target enhancement algorithm (ARTEA), a multistage preprocessing framework. ARTEA integrates dynamic range compression, continuous-scale normalization guided by adaptive sigma maps, and a frequency-domain refinement step. By dynamically adjusting parameters according to local signal characteristics, ARTEA is designed to achieve an effective tradeoff between artifact suppression and target preservation. Experiments on both synthetic and field GPR data demonstrate that ARTEA can enhance target contrast and structural fidelity while suppressing artifacts and preserving essential target features.
{"title":"ARTEA: A Multistage Adaptive Preprocessing Algorithm for Subsurface Target Enhancement in Ground Penetrating Radar","authors":"Wenqiang Ding;Changying Ma;Xintong Dong;Xuan Li","doi":"10.1109/LGRS.2025.3634350","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3634350","url":null,"abstract":"The heterogeneity of subsurface media induces multipath scattering and dielectric loss in ground penetrating radar (GPR) signal propagation, which results in wavefront distortion and signal attenuation. These effects degrade B-scan profiles by blurring target signatures, hindering automated feature extraction, and reducing the clarity of regions of interest (ROI). To address these issues, we propose the adaptive region target enhancement algorithm (ARTEA), a multistage preprocessing framework. ARTEA integrates dynamic range compression, continuous-scale normalization guided by adaptive sigma maps, and a frequency-domain refinement step. By dynamically adjusting parameters according to local signal characteristics, ARTEA is designed to achieve an effective tradeoff between artifact suppression and target preservation. Experiments on both synthetic and field GPR data demonstrate that ARTEA can enhance target contrast and structural fidelity while suppressing artifacts and preserving essential target features.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Remote sensing (RS) image scene classification has wide applications in the field of RS. Although the existing methods have achieved remarkable performance, there are still limitations in feature extraction and lightweight design. Current multibranch models, although performing well, have large parameter counts and high computational costs, making them difficult to deploy on resource-constrained edge devices, such as uncrewed aerial vehicles (UAVs). On the other hand, lightweight models like StarNet, having less parameter, but rely on elementwise multiplication to generate features and lack the capture of explicit long-range spatial feature, resulting in insufficient classification accuracy. To address these issues, this letter proposes a lightweight mamba-based hybrid network, namely LMHMamba, whose core is an innovative lightweight multifeature hybrid Mamba (LMHM) module. This module combines the advantage of StarNet in implicitly generating high-dimensional nonlinear features, introduces a lightweight state-space module to enhance spatial feature learning capabilities, and then uses local and global attention modules to emphasize local and global features. This enables effective multidimensional feature fusion while maintaining low parameter. We validate the performance of LMHMamba model on three RS scene classification datasets and compare it with mainstream lightweight models and the latest methods. Experimental results show that LMHMamba achieves advanced levels in both classification accuracy and computational efficiency, significantly outperforming the existing lightweight models, providing an efficient solution for edge deployment. Code is available at https://github.com/yizhilanmaodhh/LMHMamba
{"title":"A Lightweight Multifeature Hybrid Mamba for Remote Sensing Image Scene Classification","authors":"Huihui Dong;Jingcao Li;Zongfang Ma;Zhijie Li;Mengkun Liu;Xiaohui Wei;Licheng Jiao","doi":"10.1109/LGRS.2025.3634398","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3634398","url":null,"abstract":"Remote sensing (RS) image scene classification has wide applications in the field of RS. Although the existing methods have achieved remarkable performance, there are still limitations in feature extraction and lightweight design. Current multibranch models, although performing well, have large parameter counts and high computational costs, making them difficult to deploy on resource-constrained edge devices, such as uncrewed aerial vehicles (UAVs). On the other hand, lightweight models like StarNet, having less parameter, but rely on elementwise multiplication to generate features and lack the capture of explicit long-range spatial feature, resulting in insufficient classification accuracy. To address these issues, this letter proposes a lightweight mamba-based hybrid network, namely LMHMamba, whose core is an innovative lightweight multifeature hybrid Mamba (LMHM) module. This module combines the advantage of StarNet in implicitly generating high-dimensional nonlinear features, introduces a lightweight state-space module to enhance spatial feature learning capabilities, and then uses local and global attention modules to emphasize local and global features. This enables effective multidimensional feature fusion while maintaining low parameter. We validate the performance of LMHMamba model on three RS scene classification datasets and compare it with mainstream lightweight models and the latest methods. Experimental results show that LMHMamba achieves advanced levels in both classification accuracy and computational efficiency, significantly outperforming the existing lightweight models, providing an efficient solution for edge deployment. Code is available at <uri>https://github.com/yizhilanmaodhh/LMHMamba</uri>","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145612119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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