Pub Date : 2026-01-29DOI: 10.1109/LGRS.2026.3654221
Ming Li;Jiahua Zhang;Jan-Peter Weiss;John J. Braun;William Gullotta;Maggie Sleziak-Sallee
Presents corrections to the paper, Corrections to “Spire Near-Nadir GNSS-R for Sea Ice Detection: First Results”.
对“尖塔近最低点GNSS-R海冰探测：初步结果”的论文进行修正。
{"title":"Corrections to “Spire Near-Nadir GNSS-R for Sea Ice Detection: First Results”","authors":"Ming Li;Jiahua Zhang;Jan-Peter Weiss;John J. Braun;William Gullotta;Maggie Sleziak-Sallee","doi":"10.1109/LGRS.2026.3654221","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3654221","url":null,"abstract":"Presents corrections to the paper, Corrections to “Spire Near-Nadir GNSS-R for Sea Ice Detection: First Results”.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-3"},"PeriodicalIF":4.4,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11368646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082089","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 : 2026-01-12DOI: 10.1109/LGRS.2026.3652414
Hongbin Lu;Jie Shao;Yibo Wang
Airborne sound sources generate strong pressure disturbances that couple with the ground to produce acoustic-to-seismic signals, leaving a continuous seismic footprint detectable by sensors. This study simulates the seismic response of moving aircraft using the finite difference method (FDM) and analyzes the relationship between signal amplitude and the distance to the flight path. By integrating the wavefront diffusion equation with the distance formula, we derive a mathematical link between maximum amplitude and trajectory parameters. Based on this relation, an inversion algorithm using linear array data is developed for trajectory tracking. Field data from Beijing Capital International Airport validate the feasibility of the method. Results demonstrate that the proposed approach enables reliable large-scale and long-term aircraft monitoring, complementing traditional radar and acoustic technologies for aviation surveillance.
{"title":"Acoustic-to-Seismic Signal Attenuation and Aircraft Trajectory Tracking","authors":"Hongbin Lu;Jie Shao;Yibo Wang","doi":"10.1109/LGRS.2026.3652414","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3652414","url":null,"abstract":"Airborne sound sources generate strong pressure disturbances that couple with the ground to produce acoustic-to-seismic signals, leaving a continuous seismic footprint detectable by sensors. This study simulates the seismic response of moving aircraft using the finite difference method (FDM) and analyzes the relationship between signal amplitude and the distance to the flight path. By integrating the wavefront diffusion equation with the distance formula, we derive a mathematical link between maximum amplitude and trajectory parameters. Based on this relation, an inversion algorithm using linear array data is developed for trajectory tracking. Field data from Beijing Capital International Airport validate the feasibility of the method. Results demonstrate that the proposed approach enables reliable large-scale and long-term aircraft monitoring, complementing traditional radar and acoustic technologies for aviation surveillance.","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":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026393","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 : 2026-01-12DOI: 10.1109/LGRS.2026.3653619
Weiyong Tang;Xiao Yang;Haihe Zhou;Yingli Liu
Remote sensing image object detection is characterized by dim features of small objects and complex backgrounds. In most networks, simply enhancing small object features may disrupt global consistency and affect detection results. This letter designs a region-aware refinement module (RARM) to locate the enhanced target semantic representation and suppress background noise interference and a dual detection branch to fuse underlying details and shallow semantic features at the neck of the feature pyramid, enhancing the model’s ability to focus on very small targets. Experimental results show that the improved model achieves an mAP50% of 76.0% on the Vehicle Detection in AI (VEDAI) dataset, which is 10.6% higher than the original YOLOv8s. The mAP${}_{mathrm {50-95}}$ % is 46.5%, which is 7% higher. The precision and recall rates are improved by 7.5% and 12.2%, respectively. The generalization performance was verified on VisDrone2019 and SODA-A remote sensing datasets, with mAP50% of 47.1% and 73.8%, respectively, a model size of only 29.5 MB, balancing lightweight and high detection performance. This method provides a technical approach involving the cooperative optimization of multiple modules for target detection in complex remote sensing scenes, offering significant application value.
{"title":"RARM-YOLO: Remote Sensing Small-Target Detection Model Enhanced by Dual-Branch Region-Aware Refinement Module","authors":"Weiyong Tang;Xiao Yang;Haihe Zhou;Yingli Liu","doi":"10.1109/LGRS.2026.3653619","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3653619","url":null,"abstract":"Remote sensing image object detection is characterized by dim features of small objects and complex backgrounds. In most networks, simply enhancing small object features may disrupt global consistency and affect detection results. This letter designs a region-aware refinement module (RARM) to locate the enhanced target semantic representation and suppress background noise interference and a dual detection branch to fuse underlying details and shallow semantic features at the neck of the feature pyramid, enhancing the model’s ability to focus on very small targets. Experimental results show that the improved model achieves an mAP50% of 76.0% on the Vehicle Detection in AI (VEDAI) dataset, which is 10.6% higher than the original YOLOv8s. The mAP<inline-formula> <tex-math>${}_{mathrm {50-95}}$ </tex-math></inline-formula>% is 46.5%, which is 7% higher. The precision and recall rates are improved by 7.5% and 12.2%, respectively. The generalization performance was verified on VisDrone2019 and SODA-A remote sensing datasets, with mAP50% of 47.1% and 73.8%, respectively, a model size of only 29.5 MB, balancing lightweight and high detection performance. This method provides a technical approach involving the cooperative optimization of multiple modules for target detection in complex remote sensing scenes, offering significant application value.","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":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081995","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 : 2026-01-12DOI: 10.1109/LGRS.2026.3652424
Jian Hui;Jie Liu;Xue Liu;Jian Zhu;Yanhong Duan;Xin Ye
The thermal infrared (TIR) band of the Landsat-9 satellite has a spatial resolution of 100 m, which is coarser than the optical multispectral bands of the 30-m resolution and the panchromatic band of the 15-m resolution. Existing image fusion algorithm studies primarily focus on multispectral and panchromatic bands. For the TIR band with the lowest spatial resolution, although Landsat-9 land surface temperature (LST) products are resampled to 30 m using the cubic algorithm to match the multispectral bands, the physical mechanism of sharpening methods remains unclear, and they fail to fully leverage the higher spatial resolution advantage provided by the panchromatic band. This study proposes a practical LST sharpening method (TOA-MsPS) by integrating the correlation between top-of-atmosphere TIR brightness temperature (BT) and the fused reflectance of multispectral and panchromatic bands. The TOA-MsPS method comprises three steps: panchromatic and multispectral band fusion, correlation modeling of reflectance and BT, and LST end-to-end retrieval. Compared to existing methods, the TOA-MsPS method sharpens the spatial resolution of both TIR band BT images to 15 m without relying on external parameters, simultaneously deriving the LST data. All input data for the method consists of remote sensing observations at the TOA, reducing external parameter uncertainties and error propagation inherent in the preprocessing steps of current LST retrieval algorithms, such as atmospheric correction, resampling, and emissivity estimation. Qualitative visual interpretation based on visual inspection indicates that TOA-MsPS-derived LST images exhibit significantly enhanced detail and reasonable local spatial distribution. Quantitative validations using ground site measurements demonstrate that the sharpened LST images achieve comparable accuracy to Landsat-9 LST products while substantially improving spatial resolution, with root mean square errors of 2.45 K (LST product) and 2.35 K (LST sharpen), respectively. Furthermore, the TOA-MsPS method can be directly applied to remote sensing data from multiple other remote sensing data sources, further enhancing the precision of long-term land surface thermal radiance monitoring.
Landsat-9卫星的热红外（TIR）波段空间分辨率为100 m，比30 m分辨率的光学多光谱波段和15 m分辨率的全色波段粗糙。现有的图像融合算法研究主要集中在多光谱和全色波段。对于空间分辨率最低的TIR波段，尽管Landsat-9陆地表面温度（LST）产品使用三次算法重采样到30 m以匹配多光谱波段，但锐化方法的物理机制尚不清楚，无法充分利用全色波段提供的更高空间分辨率优势。本研究提出了一种实用的地表温度锐化方法（TOA-MsPS），该方法将大气顶TIR亮度温度（BT）与多光谱和全色波段的融合反射率相结合。TOA-MsPS方法包括三个步骤：全色和多光谱波段融合、反射率和BT的相关建模、LST端到端检索。与现有方法相比，TOA-MsPS方法在不依赖外部参数的情况下，将两个TIR波段BT图像的空间分辨率提高到15 m，同时获得地表温度数据。该方法的所有输入数据均由TOA的遥感观测数据组成，减少了当前LST检索算法预处理步骤（如大气校正、重采样和发射率估计）中固有的外部参数不确定性和误差传播。基于目视检查的定性目视解译表明，toa - msps衍生的LST图像具有明显增强的细节和合理的局部空间分布。利用地面测量数据进行的定量验证表明，锐化后的LST图像的精度与Landsat-9的LST产品相当，同时显著提高了空间分辨率，均方根误差分别为2.45 K （LST产品）和2.35 K （LST锐化）。此外，TOA-MsPS方法可直接应用于其他多个遥感数据源的遥感数据，进一步提高了地表热辐射长期监测的精度。
{"title":"A Practical Landsat-9 Land Surface Temperature Sharpen Method Combining Top-of-Atmosphere Multispectral and Panchromatic Reflectance","authors":"Jian Hui;Jie Liu;Xue Liu;Jian Zhu;Yanhong Duan;Xin Ye","doi":"10.1109/LGRS.2026.3652424","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3652424","url":null,"abstract":"The thermal infrared (TIR) band of the Landsat-9 satellite has a spatial resolution of 100 m, which is coarser than the optical multispectral bands of the 30-m resolution and the panchromatic band of the 15-m resolution. Existing image fusion algorithm studies primarily focus on multispectral and panchromatic bands. For the TIR band with the lowest spatial resolution, although Landsat-9 land surface temperature (LST) products are resampled to 30 m using the cubic algorithm to match the multispectral bands, the physical mechanism of sharpening methods remains unclear, and they fail to fully leverage the higher spatial resolution advantage provided by the panchromatic band. This study proposes a practical LST sharpening method (TOA-MsPS) by integrating the correlation between top-of-atmosphere TIR brightness temperature (BT) and the fused reflectance of multispectral and panchromatic bands. The TOA-MsPS method comprises three steps: panchromatic and multispectral band fusion, correlation modeling of reflectance and BT, and LST end-to-end retrieval. Compared to existing methods, the TOA-MsPS method sharpens the spatial resolution of both TIR band BT images to 15 m without relying on external parameters, simultaneously deriving the LST data. All input data for the method consists of remote sensing observations at the TOA, reducing external parameter uncertainties and error propagation inherent in the preprocessing steps of current LST retrieval algorithms, such as atmospheric correction, resampling, and emissivity estimation. Qualitative visual interpretation based on visual inspection indicates that TOA-MsPS-derived LST images exhibit significantly enhanced detail and reasonable local spatial distribution. Quantitative validations using ground site measurements demonstrate that the sharpened LST images achieve comparable accuracy to Landsat-9 LST products while substantially improving spatial resolution, with root mean square errors of 2.45 K (LST product) and 2.35 K (LST sharpen), respectively. Furthermore, the TOA-MsPS method can be directly applied to remote sensing data from multiple other remote sensing data sources, further enhancing the precision of long-term land surface thermal radiance monitoring.","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":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081999","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}
Marchenko imaging provides a powerful framework for reconstructing amplitude-preserved subsurface images while suppressing migration artifacts caused by internal multiples. This capability is achieved by applying an energy-compensated cross correlation imaging condition to the up- and down-going Green’s functions retrieved through the Marchenko scheme. However, the performance of this approach strongly depends on the choice of the integration range: an excessively large range lowers resolution in shallow areas, whereas an overly small range can degrade the imaging quality of steeply dipping structures. To address this limitation, we propose an adaptive strategy that improves image quality by optimizing the integration range for each imaging point. The integration range is characterized by two parameters—the location of the integration center and the integration radius. Specifically, the radius is determined from the travel time difference between the up- and down-going Green’s functions together with the depth of the imaging point, while the center location is constrained by stratal dip. The resulting optimal integration range, defined by these two parameters, is then applied to both the retrieval of Marchenko Green’s functions and the subsequent imaging. The proposed method was first tested on synthetic data, where it was shown to outperform fixed integration ranges (either too large or too small) by simultaneously enhancing shallow resolution and preserving the fidelity of steeply dipping structures. It was then further applied to field data from western China, which confirmed the feasibility of the approach.
{"title":"Incorporating Stratal Dip to Constrain the Integration Range of Marchenko Imaging","authors":"Xiaochun Chen;Tianjing Shen;Dong Zhang;Yukai Wo;Haoyang Ou;Xuri Huang","doi":"10.1109/LGRS.2025.3648652","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3648652","url":null,"abstract":"Marchenko imaging provides a powerful framework for reconstructing amplitude-preserved subsurface images while suppressing migration artifacts caused by internal multiples. This capability is achieved by applying an energy-compensated cross correlation imaging condition to the up- and down-going Green’s functions retrieved through the Marchenko scheme. However, the performance of this approach strongly depends on the choice of the integration range: an excessively large range lowers resolution in shallow areas, whereas an overly small range can degrade the imaging quality of steeply dipping structures. To address this limitation, we propose an adaptive strategy that improves image quality by optimizing the integration range for each imaging point. The integration range is characterized by two parameters—the location of the integration center and the integration radius. Specifically, the radius is determined from the travel time difference between the up- and down-going Green’s functions together with the depth of the imaging point, while the center location is constrained by stratal dip. The resulting optimal integration range, defined by these two parameters, is then applied to both the retrieval of Marchenko Green’s functions and the subsequent imaging. The proposed method was first tested on synthetic data, where it was shown to outperform fixed integration ranges (either too large or too small) by simultaneously enhancing shallow resolution and preserving the fidelity of steeply dipping structures. It was then further applied to field data from western China, which confirmed the feasibility of the approach.","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":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929486","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}
Weakly supervised semantic segmentation (WSSS) based on image-level labels significantly reduces the labeling burden. However, current mainstream approaches optimize solely using single-image information, neglecting the rich semantic correlation among images and struggling to dynamically suppress interfering information. When confronted with complex backgrounds and multicategory remote sensing (RS) images, intraclass consistency and interclass discrimination pose significant challenges. To address these challenges, this letter proposes the cross-image class token constraints network (CICTC-Net). CICTC-Net establishes semantic correlations across multicategory RS images and implements two modules for targeted optimization. Specifically, the cross-image token enhancement (CITE) module constructs intraclass token relationship graphs and applies cross-image consistency constraints to enhance semantic consistency among objects of the same category. The class-patch interaction refinement (CPIR) module constructs a directed graph of class-patch relationships and employs a neighborhood selection mechanism to refine class tokens, thereby enhancing interclass discriminability. Experiments on two RS datasets demonstrate that this approach significantly outperforms existing state-of-the-art solutions.
{"title":"Weakly Supervised Semantic Segmentation of Remote Sensing Scenes With Cross-Image Class Token Constraints","authors":"Pengcheng Guo;Zhen Wang;Junhuan Peng;Yuebin Wang;Guodong Liu;Yasong Mi;Dengxiang Wu;Jie Huang","doi":"10.1109/LGRS.2025.3645679","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3645679","url":null,"abstract":"Weakly supervised semantic segmentation (WSSS) based on image-level labels significantly reduces the labeling burden. However, current mainstream approaches optimize solely using single-image information, neglecting the rich semantic correlation among images and struggling to dynamically suppress interfering information. When confronted with complex backgrounds and multicategory remote sensing (RS) images, intraclass consistency and interclass discrimination pose significant challenges. To address these challenges, this letter proposes the cross-image class token constraints network (CICTC-Net). CICTC-Net establishes semantic correlations across multicategory RS images and implements two modules for targeted optimization. Specifically, the cross-image token enhancement (CITE) module constructs intraclass token relationship graphs and applies cross-image consistency constraints to enhance semantic consistency among objects of the same category. The class-patch interaction refinement (CPIR) module constructs a directed graph of class-patch relationships and employs a neighborhood selection mechanism to refine class tokens, thereby enhancing interclass discriminability. Experiments on two RS datasets demonstrate that this approach significantly outperforms existing state-of-the-art solutions.","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":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830879","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 : 2025-12-18DOI: 10.1109/LGRS.2025.3646064
T. V. Jayakumar;Deepak Mishra;Anandakumar M. Ramiya;Jai G. Singla
This study focuses on the problem of accurately delineating connected road structures from high-resolution remote sensing imagery-an important task with broad implications for smart city development, routing systems, and emergency management. Existing convolutional and transformer-based segmentation methods often struggle to capture fine structural details, maintain road connectivity, and preserve directional continuity. In this work, we propose fractional Kolmogorov–Arnold networks (fKANs)-UNet, a novel encoder–decoder architecture designed to address these challenges. The network primarily utilizes directional strip convolutions and a feature selective fusion (FSF) block enhanced by a squeeze-and-excitation (SE) mechanism to refine feature representation. To further improve nonlinear modeling and spectral selectivity, we incorporate fractional Jacobi neural blocks (fJNBs) into the architecture. These blocks perform spectral transformations based on Jacobi’s polynomials of fractional order, enabling the model to effectively learn intricate spatial relationships and structures. To optimize the training, a hybrid objective is utilized, integrating binary cross-entropy (BCE), dice, and boundary-based terms, which collectively enhance pixelwise accuracy and edge consistency. A comprehensive evaluation, including detailed ablation analysis, was carried out using the MIT and DeepGlobe benchmark datasets. Compared to MSMDFFNet, fKAN-UNet achieves a 1.99% gain in IoU and a 1.54% boost in $F1$ score on the Massachusetts dataset. On the DeepGlobe dataset, it shows a 0.53% increase in IoU along with a 0.35% enhancement in the F1 metric. The code is available at: https://github.com/Jayku88/fKANUNet
{"title":"fKAN-UNet: Lightweight Road Segmentation With Fractional Spectral Modeling and Directional Convolutions","authors":"T. V. Jayakumar;Deepak Mishra;Anandakumar M. Ramiya;Jai G. Singla","doi":"10.1109/LGRS.2025.3646064","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3646064","url":null,"abstract":"This study focuses on the problem of accurately delineating connected road structures from high-resolution remote sensing imagery-an important task with broad implications for smart city development, routing systems, and emergency management. Existing convolutional and transformer-based segmentation methods often struggle to capture fine structural details, maintain road connectivity, and preserve directional continuity. In this work, we propose fractional Kolmogorov–Arnold networks (fKANs)-UNet, a novel encoder–decoder architecture designed to address these challenges. The network primarily utilizes directional strip convolutions and a feature selective fusion (FSF) block enhanced by a squeeze-and-excitation (SE) mechanism to refine feature representation. To further improve nonlinear modeling and spectral selectivity, we incorporate fractional Jacobi neural blocks (fJNBs) into the architecture. These blocks perform spectral transformations based on Jacobi’s polynomials of fractional order, enabling the model to effectively learn intricate spatial relationships and structures. To optimize the training, a hybrid objective is utilized, integrating binary cross-entropy (BCE), dice, and boundary-based terms, which collectively enhance pixelwise accuracy and edge consistency. A comprehensive evaluation, including detailed ablation analysis, was carried out using the MIT and DeepGlobe benchmark datasets. Compared to MSMDFFNet, fKAN-UNet achieves a 1.99% gain in IoU and a 1.54% boost in <inline-formula> <tex-math>$F1$ </tex-math></inline-formula> score on the Massachusetts dataset. On the DeepGlobe dataset, it shows a 0.53% increase in IoU along with a 0.35% enhancement in the F1 metric. The code is available at: <uri>https://github.com/Jayku88/fKANUNet</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":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886687","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 : 2025-12-17DOI: 10.1109/LGRS.2025.3645601
Wojciech T. Witkowski;Artur Guzy;Magdalena Łucka;Krzysztof Kusztykiewicz
Sinkholes generated above flooded underground mines pose a growing hazard, yet practical precursors remain lacking. We analyze five years (2018–2023) of Sentinel-1 C-band radar and Sentinel-2 multispectral imagery over the abandoned Olkusz–Pomorzany underground zinc–lead mine (Poland), where 19 sinkholes appeared during groundwater rebound in early 2022. Three hydro-spectral proxies (HSPs), C-band backscatter coefficient, moisture index (MI), and normalized difference vegetation index (NDVI), were extracted for sinkhole pixels and 500 randomly distributed background control pixels. A control–minus–event difference isolates local effects from regional variability. Welch t, Mann–Whitney U, and bootstrap tests all indicate highly significant post-collapse mean shifts in the three HSPs. Breakpoint analysis applied to the 26-month pre-event record reveals a common structural change in mid-2021, approximately six months before the first sinkhole and coincident with rapidly rising groundwater. The Chow test confirms a significant difference in regression coefficients across the detected break. These results demonstrate that freely available hydro-spectral data can supplement synthetic aperture radar interferometry-based deformation measurements, offering weeks to months of lead time for early warning in vegetated, post-mining terrain. The approach is inexpensive, transferable, and readily automated within Google Earth Engine (GEE) and R.
{"title":"Hydro-Spectral Sentinel-1/2 Precursors of Sinkhole Occurrence in a Flooded Post-Mining Area","authors":"Wojciech T. Witkowski;Artur Guzy;Magdalena Łucka;Krzysztof Kusztykiewicz","doi":"10.1109/LGRS.2025.3645601","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3645601","url":null,"abstract":"Sinkholes generated above flooded underground mines pose a growing hazard, yet practical precursors remain lacking. We analyze five years (2018–2023) of Sentinel-1 C-band radar and Sentinel-2 multispectral imagery over the abandoned Olkusz–Pomorzany underground zinc–lead mine (Poland), where 19 sinkholes appeared during groundwater rebound in early 2022. Three hydro-spectral proxies (HSPs), C-band backscatter coefficient, moisture index (MI), and normalized difference vegetation index (NDVI), were extracted for sinkhole pixels and 500 randomly distributed background control pixels. A control–minus–event difference isolates local effects from regional variability. Welch t, Mann–Whitney U, and bootstrap tests all indicate highly significant post-collapse mean shifts in the three HSPs. Breakpoint analysis applied to the 26-month pre-event record reveals a common structural change in mid-2021, approximately six months before the first sinkhole and coincident with rapidly rising groundwater. The Chow test confirms a significant difference in regression coefficients across the detected break. These results demonstrate that freely available hydro-spectral data can supplement synthetic aperture radar interferometry-based deformation measurements, offering weeks to months of lead time for early warning in vegetated, post-mining terrain. The approach is inexpensive, transferable, and readily automated within Google Earth Engine (GEE) and <monospace>R</monospace>.","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":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929472","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 : 2025-12-17DOI: 10.1109/LGRS.2025.3645558
Yuanyuan Dang;Wenhao Liu;Bing Liu;Hao Li
To address the challenges of incomplete pseudo-labels and complex background interference in semi-supervised remote sensing road extraction, we propose a multistructure adaptive generative adversarial network (MSA-GAN). The generator integrates a wavelet attention feature fusion module (WAFFM) and a multiscale context and detail enhancement module (MCDEM) to extract hierarchical structural cues and preserve road continuity. WAFFM combines wavelet-based decomposition and directional attention to enhance edge connectivity, while MCDEM aggregates contextual semantics and local details via strip pooling and enhanced atrous convolutions. The discriminator incorporates a rectangular self-calibration module (RCM) to capture directional dependencies, and a dynamic feature adaptation module (DFAM) to adaptively suppress structural and semantic noise through deformable convolutions and dynamic fusion. These modules establish a structure-aware adversarial framework, enhancing both pseudo-label quality and segmentation consistency. Experiments on DeepGlobe and Massachusetts datasets demonstrate that MSA-GAN achieves consistent improvements in ${F}1$ and IoU (0.89%–6.0%) over state-of-the-art methods, validating the effectiveness of multistructure enhancements and adaptive adversarial learning in semi-supervised road extraction.
{"title":"MSA-GAN: Multistructure Adaptive Generative Adversarial Network for Semi-Supervised Remote Sensing Road Extraction","authors":"Yuanyuan Dang;Wenhao Liu;Bing Liu;Hao Li","doi":"10.1109/LGRS.2025.3645558","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3645558","url":null,"abstract":"To address the challenges of incomplete pseudo-labels and complex background interference in semi-supervised remote sensing road extraction, we propose a multistructure adaptive generative adversarial network (MSA-GAN). The generator integrates a wavelet attention feature fusion module (WAFFM) and a multiscale context and detail enhancement module (MCDEM) to extract hierarchical structural cues and preserve road continuity. WAFFM combines wavelet-based decomposition and directional attention to enhance edge connectivity, while MCDEM aggregates contextual semantics and local details via strip pooling and enhanced atrous convolutions. The discriminator incorporates a rectangular self-calibration module (RCM) to capture directional dependencies, and a dynamic feature adaptation module (DFAM) to adaptively suppress structural and semantic noise through deformable convolutions and dynamic fusion. These modules establish a structure-aware adversarial framework, enhancing both pseudo-label quality and segmentation consistency. Experiments on DeepGlobe and Massachusetts datasets demonstrate that MSA-GAN achieves consistent improvements in <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula> and IoU (0.89%–6.0%) over state-of-the-art methods, validating the effectiveness of multistructure enhancements and adaptive adversarial learning in semi-supervised road extraction.","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":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830849","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 : 2025-12-17DOI: 10.1109/LGRS.2025.3645669
Shuying Li;Qiang Ma;San Zhang;Wuwei Wang;Chuang Yang
Infrared small target detection (IRSTD) plays a crucial role in numerous military and civilian applications. However, the existing methods often face the gradual degradation of target edge pixels as the number of network layers increases, and traditional convolution struggles to differentiate between frequency components during feature extraction, leading to low-frequency backgrounds interfering with high-frequency targets and high-frequency noise triggering false detections. To address these limitations, we propose multiscale differential edge and adaptive frequency guided network for IRSTD (MDAFNet), which integrates the multiscale differential edge (MSDE) module and dual-domain adaptive feature enhancement (DAFE) module. The MSDE module, through a multiscale edge extraction and enhancement mechanism, effectively compensates for the cumulative loss of target edge information during downsampling. The DAFE module combines frequency-domain processing mechanisms with simulated frequency decomposition and fusion mechanisms in the spatial domain to effectively improve the network’s capability to adaptively enhance high-frequency targets and selectively suppress high-frequency noise. Experimental results on multiple datasets demonstrate the superior detection performance of MDAFNet.
{"title":"MDAFNet: Multiscale Differential Edge and Adaptive Frequency Guided Network for Infrared Small Target Detection","authors":"Shuying Li;Qiang Ma;San Zhang;Wuwei Wang;Chuang Yang","doi":"10.1109/LGRS.2025.3645669","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3645669","url":null,"abstract":"Infrared small target detection (IRSTD) plays a crucial role in numerous military and civilian applications. However, the existing methods often face the gradual degradation of target edge pixels as the number of network layers increases, and traditional convolution struggles to differentiate between frequency components during feature extraction, leading to low-frequency backgrounds interfering with high-frequency targets and high-frequency noise triggering false detections. To address these limitations, we propose multiscale differential edge and adaptive frequency guided network for IRSTD (MDAFNet), which integrates the multiscale differential edge (MSDE) module and dual-domain adaptive feature enhancement (DAFE) module. The MSDE module, through a multiscale edge extraction and enhancement mechanism, effectively compensates for the cumulative loss of target edge information during downsampling. The DAFE module combines frequency-domain processing mechanisms with simulated frequency decomposition and fusion mechanisms in the spatial domain to effectively improve the network’s capability to adaptively enhance high-frequency targets and selectively suppress high-frequency noise. Experimental results on multiple datasets demonstrate the superior detection performance of MDAFNet.","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":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830839","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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