Pub Date : 2025-09-04DOI: 10.1109/LGRS.2025.3605916
Yuquan Gan;Siyu Wu;Xingyu Li;Zhijie Xu;Yushan Pan
Hyperspectral image (HSI) classification faces critical challenges in effectively modeling the intricate spectral–spatial structures and non-Euclidean relationships. Traditional methods often struggle to simultaneously capture local details, global contextual dependencies, and graph-structured correlations, leading to limited classification accuracy. To address the above issues, this letter proposes a graph-aware hybrid encoding (GAHE) framework. To fully exploit the spectral–spatial characteristics and graph structural dependencies inherent in HSI, the proposed method is structured into three key components: a multiscale selective graph-aware attention (MSGA) module, a hybrid projection encoding module, and a graph sensitive aggregation (GSA) module. The three modules work in a complementary manner to progressively refine and enhance feature representations across multiple scales and modalities. Compared with advanced classification methods, the experimental results demonstrate that the proposed GAHE method shows better classification performance.
{"title":"Graph-Aware Hybrid Encoding for Hyperspectral Image Classification","authors":"Yuquan Gan;Siyu Wu;Xingyu Li;Zhijie Xu;Yushan Pan","doi":"10.1109/LGRS.2025.3605916","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3605916","url":null,"abstract":"Hyperspectral image (HSI) classification faces critical challenges in effectively modeling the intricate spectral–spatial structures and non-Euclidean relationships. Traditional methods often struggle to simultaneously capture local details, global contextual dependencies, and graph-structured correlations, leading to limited classification accuracy. To address the above issues, this letter proposes a graph-aware hybrid encoding (GAHE) framework. To fully exploit the spectral–spatial characteristics and graph structural dependencies inherent in HSI, the proposed method is structured into three key components: a multiscale selective graph-aware attention (MSGA) module, a hybrid projection encoding module, and a graph sensitive aggregation (GSA) module. The three modules work in a complementary manner to progressively refine and enhance feature representations across multiple scales and modalities. Compared with advanced classification methods, the experimental results demonstrate that the proposed GAHE method shows better classification performance.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110260","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 : 2025-09-04DOI: 10.1109/LGRS.2025.3605978
Allan A. Nielsen;Henning Skriver;Knut Conradsen
We report on a complex Wishart distribution-based test statistic $boldsymbol {Q}$ for block-diagonality in Hermitian matrices such as the ones analyzed in polarimetric synthetic aperture radar (polSAR) image data in the covariance matrix formulation. We also give an improved probability measure $boldsymbol {P}$ associated with the test statistic. This is used in a case with simulated data to demonstrate the superiority of the new expression for $boldsymbol {P}$ and to illustrate the dependence of results on the choice of covariance matrix, its dimensionality, the equivalent number of looks, and two parameters in the improved $boldsymbol {P}$ measure. We also give two cases with acquired data. One case is with airborne F-SAR polarimetric data, where we test for reflection symmetry, another case is with (spaceborne) dual-pol Sentinel-1 data, where we test if the data are diagonal-only. The absence of block-diagonal structure occurs mostly for man-made objects. In the example with Sentinel-1 data, some objects (e.g., buildings, cars, aircraft, and ships) are detected, others (e.g., some bridges) are not.
{"title":"A Test Statistic for Block-Diagonal Covariance Matrix Structure in polSAR Data","authors":"Allan A. Nielsen;Henning Skriver;Knut Conradsen","doi":"10.1109/LGRS.2025.3605978","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3605978","url":null,"abstract":"We report on a complex Wishart distribution-based test statistic <inline-formula> <tex-math>$boldsymbol {Q}$ </tex-math></inline-formula> for block-diagonality in Hermitian matrices such as the ones analyzed in polarimetric synthetic aperture radar (polSAR) image data in the covariance matrix formulation. We also give an improved probability measure <inline-formula> <tex-math>$boldsymbol {P}$ </tex-math></inline-formula> associated with the test statistic. This is used in a case with simulated data to demonstrate the superiority of the new expression for <inline-formula> <tex-math>$boldsymbol {P}$ </tex-math></inline-formula> and to illustrate the dependence of results on the choice of covariance matrix, its dimensionality, the equivalent number of looks, and two parameters in the improved <inline-formula> <tex-math>$boldsymbol {P}$ </tex-math></inline-formula> measure. We also give two cases with acquired data. One case is with airborne F-SAR polarimetric data, where we test for reflection symmetry, another case is with (spaceborne) dual-pol Sentinel-1 data, where we test if the data are diagonal-only. The absence of block-diagonal structure occurs mostly for man-made objects. In the example with Sentinel-1 data, some objects (e.g., buildings, cars, aircraft, and ships) are detected, others (e.g., some bridges) are not.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061854","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 : 2025-09-03DOI: 10.1109/LGRS.2025.3605792
Siyuan Ding;Xun Wang;Deshan Feng;Cheng Chen;Dianbo Li
Ground penetrating radar (GPR) is a powerful tool for exploring the shallow subsurface due to its effective and noninvasive features. Recently, the accurate and high-resolution characterization of subsurface properties in 3-D GPR investigations calls for a quantitative and high-resolution imaging approach. However, the full-waveform inversion (FWI) method for GPR data was performed mostly in 2-D and rarely discussed the polarizations. To fully utilize 3-D GPR polarization data, this letter proposes a frequency-domain FWI algorithm for simultaneous inversion of both the co-polarized and cross-polarized data. Detail derivations and vital processes in our inversion workflow were described in detail, before applying it to the numerical experiments and analyzing the potential impacts of the polarizations on inversion results with a synthetic model. Results showed that the cross-polarized data are more sensitive than the co-polarized data in inversion, and the behaviors in the inversion of the multipolarized data with different values in the weighting matrix suggest that larger weights for co-polarized data are of benefit to a better inversion result.
{"title":"Potential Impacts of 3-D Polarized GPR Data on Full-Waveform Inversion","authors":"Siyuan Ding;Xun Wang;Deshan Feng;Cheng Chen;Dianbo Li","doi":"10.1109/LGRS.2025.3605792","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3605792","url":null,"abstract":"Ground penetrating radar (GPR) is a powerful tool for exploring the shallow subsurface due to its effective and noninvasive features. Recently, the accurate and high-resolution characterization of subsurface properties in 3-D GPR investigations calls for a quantitative and high-resolution imaging approach. However, the full-waveform inversion (FWI) method for GPR data was performed mostly in 2-D and rarely discussed the polarizations. To fully utilize 3-D GPR polarization data, this letter proposes a frequency-domain FWI algorithm for simultaneous inversion of both the co-polarized and cross-polarized data. Detail derivations and vital processes in our inversion workflow were described in detail, before applying it to the numerical experiments and analyzing the potential impacts of the polarizations on inversion results with a synthetic model. Results showed that the cross-polarized data are more sensitive than the co-polarized data in inversion, and the behaviors in the inversion of the multipolarized data with different values in the weighting matrix suggest that larger weights for co-polarized data are of benefit to a better inversion result.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090173","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}
The Surface Water and Ocean Topography (SWOT) satellite provides high-resolution observations of the ocean surface topography and elevation of inland waters. Measurements from the two onboard Advanced Microwave Radiometers (AMRs) are used to compute the wet tropospheric correction (WTC), accounting for the radar signal delay due to water vapor and cloud liquid water content in the troposphere. This study presents the first implementation of the Global Navigation Satellite System (GNSS)-derived Path Delay Plus (GPD+) algorithm for SWOT to estimate the WTC when AMR observations are absent or invalid. Using the first 15 science-phase cycles between 50°N and 50°S, GPD+ retrieves the WTC for approximately 7% of points per cycle that would otherwise be excluded. Retrieval rates per cycle range from less than 5% of the points in passes mostly over open ocean, where the WTC derived from the radiometers is usually preserved, to up to 15% in passes including coastal zones. These results indicate that GPD+ can recover WTC values otherwise unavailable from SWOT’s radiometers, increasing the availability of valid WTC for SWOT measurements, in particular over coastal regions. Further refinements will focus on improving the accuracy of the WTC along the KaRIn swath and the Poseidon-3C nadir track.
{"title":"First Implementation of GPD+ Wet Tropospheric Correction on SWOT Side 1 and Side 2 Radiometer Tracks","authors":"Isabel Cardoso;Clara Lázaro;Telmo Vieira;M. Joana Fernandes","doi":"10.1109/LGRS.2025.3605854","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3605854","url":null,"abstract":"The Surface Water and Ocean Topography (SWOT) satellite provides high-resolution observations of the ocean surface topography and elevation of inland waters. Measurements from the two onboard Advanced Microwave Radiometers (AMRs) are used to compute the wet tropospheric correction (WTC), accounting for the radar signal delay due to water vapor and cloud liquid water content in the troposphere. This study presents the first implementation of the Global Navigation Satellite System (GNSS)-derived Path Delay Plus (GPD+) algorithm for SWOT to estimate the WTC when AMR observations are absent or invalid. Using the first 15 science-phase cycles between 50°N and 50°S, GPD+ retrieves the WTC for approximately 7% of points per cycle that would otherwise be excluded. Retrieval rates per cycle range from less than 5% of the points in passes mostly over open ocean, where the WTC derived from the radiometers is usually preserved, to up to 15% in passes including coastal zones. These results indicate that GPD+ can recover WTC values otherwise unavailable from SWOT’s radiometers, increasing the availability of valid WTC for SWOT measurements, in particular over coastal regions. Further refinements will focus on improving the accuracy of the WTC along the KaRIn swath and the Poseidon-3C nadir track.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036199","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}
Predictively synthesizing high-quality, close-range asteroid surface views from distant optical remote sensing imagery is critical for mission planning and landing-site selection in asteroid exploration missions. However, distant observations inherently lack sufficient resolution and surface detail, limiting the existing novel view synthesis (NVS) methods. To address this, we introduce, to the best of our knowledge, the first framework for distant-to-close NVS, tailored for asteroid surface imaging. Our method features two key innovations. First, a 3-D Gaussian splatting (3D-GS) super-resolution (SR) module applies 2-D SR to generate high-resolution virtual close-range views from distant images, enriching the 3-D scene model with finer details. Second, an entropy-driven residual refinement strategy adaptively emphasizes structurally complex regions by assigning higher loss weights based on residual image entropy. This strategy triggers targeted subdivisions of 3-D Gaussians in the areas of high structural complexity. Experiments conducted on datasets from Hayabusa (Itokawa), Dawn (Vesta), Rosetta (67P/Churyumov-Gerasimenko), Hayabusa2 (Ryugu), and OSIRIS-REx (Bennu) missions demonstrate substantial improvements over baseline methods in quantitative metrics, such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS).
从遥远的光学遥感影像中预测合成高质量的近距离小行星表面图像对于小行星探测任务的任务规划和着陆点选择至关重要。然而，远程观测本身缺乏足够的分辨率和表面细节，限制了现有的新视图合成（NVS）方法。为了解决这个问题，我们介绍了，据我们所知，为小行星表面成像量身定制的第一个远距离到近距离NVS框架。我们的方法有两个关键的创新。首先，3d高斯飞溅（3D-GS）超分辨率（SR）模块应用2d SR从远处图像生成高分辨率虚拟近景视图，以更精细的细节丰富3d场景模型。其次，熵驱动残差细化策略通过基于残差图像熵分配更高的损失权值，自适应地强调结构复杂的区域。该策略触发了高结构复杂性区域的三维高斯函数的目标细分。在Hayabusa (Itokawa), Dawn (Vesta), Rosetta (67P/Churyumov-Gerasimenko), Hayabusa2 （Ryugu）和OSIRIS-REx （Bennu）任务的数据集上进行的实验表明，在峰值信噪比（PSNR），结构相似指数测量（SSIM）和学习感知图像patch相似度（LPIPS）等定量指标上，比基线方法有了实质性的改进。
{"title":"Distant-to-Close Novel View Synthesis for Asteroid Surface Imaging","authors":"Xiaodong Wei;Linyan Cui;Xinyu Zhao;Gangzheng Ai;Jihao Yin","doi":"10.1109/LGRS.2025.3605777","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3605777","url":null,"abstract":"Predictively synthesizing high-quality, close-range asteroid surface views from distant optical remote sensing imagery is critical for mission planning and landing-site selection in asteroid exploration missions. However, distant observations inherently lack sufficient resolution and surface detail, limiting the existing novel view synthesis (NVS) methods. To address this, we introduce, to the best of our knowledge, the first framework for distant-to-close NVS, tailored for asteroid surface imaging. Our method features two key innovations. First, a 3-D Gaussian splatting (3D-GS) super-resolution (SR) module applies 2-D SR to generate high-resolution virtual close-range views from distant images, enriching the 3-D scene model with finer details. Second, an entropy-driven residual refinement strategy adaptively emphasizes structurally complex regions by assigning higher loss weights based on residual image entropy. This strategy triggers targeted subdivisions of 3-D Gaussians in the areas of high structural complexity. Experiments conducted on datasets from Hayabusa (Itokawa), Dawn (Vesta), Rosetta (67P/Churyumov-Gerasimenko), Hayabusa2 (Ryugu), and OSIRIS-REx (Bennu) missions demonstrate substantial improvements over baseline methods in quantitative metrics, such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS).","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090172","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 : 2025-09-02DOI: 10.1109/LGRS.2025.3605331
Fan Ye;Xiaoning Zhang;Zhengjie Wang;Yifei Wang;Zhaoyang Peng;Tengying Fu;Ziti Jiao;Yanxuan Wu;Yue Wang
Considering the simplicity of flight route planning, orthorectified images obtained from nadir observations are widely used in remote sensing. However, they are always insufficient to represent the anisotropic reflectance and 3-D structural information of objects. Therefore, multiangle observation information can enhance target information and potentially improve the accuracy of target classification and recognition. In this study, we investigated the potential of anisotropic reflectance information in land cover classification. By employing the DJI P4M multispectral observation system, multiangle multispectral reflectance images for five land cover types were captured at bare soil, concrete roads, grassland, apricot tree, and red broom cypress areas. Subsequently, the anisotropic flat index (AFX)-based bidirectional reflectance distribution function (BRDF) archetypes model and the kernel-driven model were used to reconstruct the BRDF. Finally, land cover classification was performed using three types of machine learning algorithm considering different BRDF features and band combinations. The results indicate that, compared to nadir directional reflectance, multiangle feature sets can improve the overall classification accuracy up to 24%. Compared to using single-band information, band combinations can also improve that up to 54%. The overall accuracy using the feature set of kernel-driven model parameters and nadir reflectance was also enhanced significantly, which can reach 86% using green-red-near infrared band combinations. This work demonstrates the contribution of multiangle multispectral information to natural and artificial land cover classification.
{"title":"Application of Optical Multiangle Multispectral Reflectance in Land Cover Classification","authors":"Fan Ye;Xiaoning Zhang;Zhengjie Wang;Yifei Wang;Zhaoyang Peng;Tengying Fu;Ziti Jiao;Yanxuan Wu;Yue Wang","doi":"10.1109/LGRS.2025.3605331","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3605331","url":null,"abstract":"Considering the simplicity of flight route planning, orthorectified images obtained from nadir observations are widely used in remote sensing. However, they are always insufficient to represent the anisotropic reflectance and 3-D structural information of objects. Therefore, multiangle observation information can enhance target information and potentially improve the accuracy of target classification and recognition. In this study, we investigated the potential of anisotropic reflectance information in land cover classification. By employing the DJI P4M multispectral observation system, multiangle multispectral reflectance images for five land cover types were captured at bare soil, concrete roads, grassland, apricot tree, and red broom cypress areas. Subsequently, the anisotropic flat index (AFX)-based bidirectional reflectance distribution function (BRDF) archetypes model and the kernel-driven model were used to reconstruct the BRDF. Finally, land cover classification was performed using three types of machine learning algorithm considering different BRDF features and band combinations. The results indicate that, compared to nadir directional reflectance, multiangle feature sets can improve the overall classification accuracy up to 24%. Compared to using single-band information, band combinations can also improve that up to 54%. The overall accuracy using the feature set of kernel-driven model parameters and nadir reflectance was also enhanced significantly, which can reach 86% using green-red-near infrared band combinations. This work demonstrates the contribution of multiangle multispectral information to natural and artificial land cover classification.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021449","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 : 2025-09-01DOI: 10.1109/LGRS.2025.3604651
Yutian Li;Wei Liu;Erzhu Li;Lianpeng Zhang;Xing Li
Remote sensing change detection (RSCD) is a key tool for environmental monitoring and resource management, playing a significant role in monitoring dynamic surface changes. In practical applications, RSCD often requires high precision and efficient detection methods. However, traditional methods tend to involve high technical complexity and a large number of parameters and are susceptible to interference from complex background noise, leading to poor performance in detecting change areas. To address these issues, this letter proposes a lightweight RSCD network, LMG-Net. The model uses a lightweight encoder and incorporates a hierarchical transformer module (HTF) to suppress background noise and minimize parameter increase, effectively extracting multilevel global features. Additionally, this letter introduces a multidimensional cooperative attention guidance (MAG) mechanism, further enhancing the ability to detect boundary changes. The model has only 3.29 M parameters and a computational load of 3.89G, demonstrating its high applicability, particularly for real-time applications in resource-constrained environments. Experimental results show that LMG-Net achieves the state-of-the-art (SOTA) ${F}1$ scores and IoU values on the WHU-CD, SYSU-CD, and LEVIR-CD+ datasets: (94.79%, 90.09%), (82.29%, 69.90%), and (84.30%, 71.14%).
遥感变化检测（RSCD）是环境监测和资源管理的重要工具，在监测地表动态变化方面发挥着重要作用。在实际应用中，RSCD往往需要高精度、高效的检测方法。然而，传统方法技术复杂，参数多，容易受到复杂背景噪声的干扰，检测变化区域的性能较差。为了解决这些问题，这封信提出了一个轻量级的RSCD网络LMG-Net。该模型采用轻量级编码器，并结合层次化变换模块（HTF）来抑制背景噪声和减小参数的增加，有效地提取了多层全局特征。此外，本文引入了多维合作注意引导（MAG）机制，进一步增强了检测边界变化的能力。该模型参数仅为3.29 M，计算负荷为3.89G，具有较高的适用性，尤其适用于资源受限环境下的实时应用。实验结果表明，LMG-Net在WHU-CD、SYSU-CD和levirr - cd +数据集上的得分和IoU值分别为（94.79%、90.09%）、（82.29%、69.90%）和（84.30%、71.14%），达到了最先进的（SOTA） ${F}1$分数和IoU值。
{"title":"LMG-Net: A Lightweight Remote Sensing Change Detection Network With Multilevel Global Features","authors":"Yutian Li;Wei Liu;Erzhu Li;Lianpeng Zhang;Xing Li","doi":"10.1109/LGRS.2025.3604651","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3604651","url":null,"abstract":"Remote sensing change detection (RSCD) is a key tool for environmental monitoring and resource management, playing a significant role in monitoring dynamic surface changes. In practical applications, RSCD often requires high precision and efficient detection methods. However, traditional methods tend to involve high technical complexity and a large number of parameters and are susceptible to interference from complex background noise, leading to poor performance in detecting change areas. To address these issues, this letter proposes a lightweight RSCD network, LMG-Net. The model uses a lightweight encoder and incorporates a hierarchical transformer module (HTF) to suppress background noise and minimize parameter increase, effectively extracting multilevel global features. Additionally, this letter introduces a multidimensional cooperative attention guidance (MAG) mechanism, further enhancing the ability to detect boundary changes. The model has only 3.29 M parameters and a computational load of 3.89G, demonstrating its high applicability, particularly for real-time applications in resource-constrained environments. Experimental results show that LMG-Net achieves the state-of-the-art (SOTA) <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula> scores and IoU values on the WHU-CD, SYSU-CD, and LEVIR-CD+ datasets: (94.79%, 90.09%), (82.29%, 69.90%), and (84.30%, 71.14%).","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027933","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 : 2025-08-29DOI: 10.1109/LGRS.2025.3604251
Qinfen Cai;Feng Zhou;Iraklis Giannakis;Sijing Liu;Xiangyun Hu
China’s first Mars mission [Tianwen-1 (TW-1)] successfully touched down in the Utopia Planitia of Mars with a rover subsurface penetrating radar (RoPeR) carried for exploring the regolith dielectric properties. Hyperbolic fitting is a conventional method to infer the subsurface material relative permittivity from ground penetrating radar (GPR) data. However, it is difficult to directly extract valid hyperbolas from the RoPeR data. Inspired by the recently developed deep learning-based geophysical inversion method to estimate the subsurface wave velocities through GPR data, an improved deep learning architecture is proposed to infer the Martian regolith relative permittivity from the RoPeR data, with self-attention (SA) and cascade modules are introduced into the network. The improved cascade and SA modules can improve the inversion efficiency and mitigate the scatter-diffraction effect of the predicted results. The inverted relative permittivity from the first 60 ns of the RoPeR data demonstrates an approximate line with a mean value of 4.73 in the regolith of interest. The very limited fluctuation of relative permittivity implies that no explicit stratification existing in the investigated regolith, agreeing with the previous studies.
{"title":"Predicting Martian Regolith Permittivity Using Deep Learning Methods—Revisiting Southern Utopia Planitia","authors":"Qinfen Cai;Feng Zhou;Iraklis Giannakis;Sijing Liu;Xiangyun Hu","doi":"10.1109/LGRS.2025.3604251","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3604251","url":null,"abstract":"China’s first Mars mission [Tianwen-1 (TW-1)] successfully touched down in the Utopia Planitia of Mars with a rover subsurface penetrating radar (RoPeR) carried for exploring the regolith dielectric properties. Hyperbolic fitting is a conventional method to infer the subsurface material relative permittivity from ground penetrating radar (GPR) data. However, it is difficult to directly extract valid hyperbolas from the RoPeR data. Inspired by the recently developed deep learning-based geophysical inversion method to estimate the subsurface wave velocities through GPR data, an improved deep learning architecture is proposed to infer the Martian regolith relative permittivity from the RoPeR data, with self-attention (SA) and cascade modules are introduced into the network. The improved cascade and SA modules can improve the inversion efficiency and mitigate the scatter-diffraction effect of the predicted results. The inverted relative permittivity from the first 60 ns of the RoPeR data demonstrates an approximate line with a mean value of 4.73 in the regolith of interest. The very limited fluctuation of relative permittivity implies that no explicit stratification existing in the investigated regolith, agreeing with the previous studies.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011320","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}
Change detection (CD) in remote sensing (RS) imagery remains challenging due to boundary ambiguity and false alarms caused by high foreground–background similarity and insufficient difference representation. To address these issues, we propose an edge-guided difference enhancement network (EGDENet). EGDENet integrates an edge-aware adaptive enhancement module (EAEM) to extract high-frequency edge cues across scales, and a channel-spatial cooperative difference module (CSCDM) to refine change features by jointly leveraging spatial and channel-wise differences. An upsampling feature fusion (UFF) further enhances robustness to scale variations and improves region consistency. Extensive experiments on two public datasets demonstrate that EGDENet achieves superior performance with clearer boundaries compared to state-of-the-art methods. Our source code is publicly available at https://github.com/adleess/-EGDENet
{"title":"Enhancing Change Detection With Edge-Guided Difference Modeling in Remote Sensing Imagery","authors":"Pengkai Wang;Fuchao Cheng;Yuan Yao;Liang Liu;Jianwei Zhang;Abdelaziz Bouras;D. Narasimhan;Ling Qin;Shaohua Wang;Chang Liu","doi":"10.1109/LGRS.2025.3604110","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3604110","url":null,"abstract":"Change detection (CD) in remote sensing (RS) imagery remains challenging due to boundary ambiguity and false alarms caused by high foreground–background similarity and insufficient difference representation. To address these issues, we propose an edge-guided difference enhancement network (EGDENet). EGDENet integrates an edge-aware adaptive enhancement module (EAEM) to extract high-frequency edge cues across scales, and a channel-spatial cooperative difference module (CSCDM) to refine change features by jointly leveraging spatial and channel-wise differences. An upsampling feature fusion (UFF) further enhances robustness to scale variations and improves region consistency. Extensive experiments on two public datasets demonstrate that EGDENet achieves superior performance with clearer boundaries compared to state-of-the-art methods. Our source code is publicly available at <uri>https://github.com/adleess/-EGDENet</uri>","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073149","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}
Semantic segmentation based on satellite image time series (SITS) is fundamental to a wide range of geospatial applications, including land cover mapping and urban development analysis. By integrating crop phenological dynamics over time, SITS provides richer spatiotemporal information than static satellite imagery. However, existing models fail to effectively process the temporal and spatial–spectral dimensions of SITS independently, leading to reduced segmentation accuracy. In this letter, we propose a temporal aggregation spatial–spectral bridge network (TAS2B-Net), a novel architecture designed to extract fine-grained crop features from SITS. The network consists of two key components: the pixel-aware grouping temporal integrator (PGTI), which captures temporal dependencies within pixel groups, and the edge-aware contextual fusion head (ECFH), which enhances spatial boundary and global structural representation. Additionally, we introduce a lightweight multiscale spectral decoder (LMSD) to aggregate contextual information across multiple spectral scales, further improving feature learning for semantic segmentation. Extensive experiments on the panoptic agricultural satellite time series (PASTIS) and MTLCC datasets show that the proposed network achieves mIoU scores of 68.91% and 84.59%, respectively, outperforming eight state-of-the-art (SOTA) methods and setting new benchmarks for SITS-based semantic segmentation.
{"title":"Bridging Temporal and Spatial–Spectral Features With Satellite Image Time Series: TAS2B-Net for Crop Semantic Segmentation","authors":"Xiaohan Luo;Hangyu Dai;Vladimir Lysenko;Jinglu Tan;Ya Guo","doi":"10.1109/LGRS.2025.3603294","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3603294","url":null,"abstract":"Semantic segmentation based on satellite image time series (SITS) is fundamental to a wide range of geospatial applications, including land cover mapping and urban development analysis. By integrating crop phenological dynamics over time, SITS provides richer spatiotemporal information than static satellite imagery. However, existing models fail to effectively process the temporal and spatial–spectral dimensions of SITS independently, leading to reduced segmentation accuracy. In this letter, we propose a temporal aggregation spatial–spectral bridge network (TAS2B-Net), a novel architecture designed to extract fine-grained crop features from SITS. The network consists of two key components: the pixel-aware grouping temporal integrator (PGTI), which captures temporal dependencies within pixel groups, and the edge-aware contextual fusion head (ECFH), which enhances spatial boundary and global structural representation. Additionally, we introduce a lightweight multiscale spectral decoder (LMSD) to aggregate contextual information across multiple spectral scales, further improving feature learning for semantic segmentation. Extensive experiments on the panoptic agricultural satellite time series (PASTIS) and MTLCC datasets show that the proposed network achieves mIoU scores of 68.91% and 84.59%, respectively, outperforming eight state-of-the-art (SOTA) methods and setting new benchmarks for SITS-based semantic segmentation.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007849","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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