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

{"title":"Multitask Change-Aware Network and Semisupervised Enhanced Multistep Training for Semantic Change Detection","authors":"Yifei Si;Jie Jiang","doi":"10.1109/JSTARS.2025.3554272","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554272","url":null,"abstract":"Semantic change detection (SCD) aims to find out where and what changes between a pair of co-registered remote sensing images. Compared to binary change detection, which only predicts the location of changes, SCD provides detailed from-to change information, helping to gain a comprehensive understanding and analysis of land cover and land use. SCD is a challenging task due to the complexity of scenes in remote sensing images and the lack of semantic labels in SCD datasets. In this work, we propose a model named Multitask Change-Aware Network (MTCAN) and a Multistep Training (MST) method for land cover semantic change detection in optical remote sensing images. To better identify fine-grained semantic changes, the MTCAN comprises feature aggregation module (FAM), spatial enhancement module (SEM), and change extraction module (CEM). FAM integrates low-level spatial details and high-level semantics from multilevel features, which helps to capture small-sized changes. SEM models long-range correlations and global context, providing global representations in binary change detection and semantic segmentation branches. CEM extracts discriminative change features by calibrating differential features with channel and spatial attention, which helps to accurately locate change areas. MST is designed to overcome the insufficient training caused by the lack of semantic labels, consisting of contrastive loss and iterative self-training. The contrastive loss supervises the semantic segmentation parts with binary change labels. In the self-training process, the trained student model is added to the teacher model ensemble that generates pseudo labels for unlabeled areas, which are then used to train the next student. MTCAN-MST achieves 23.48% SeK on SECOND dataset and 67.74% SeK on Landsat-SCD dataset, outperforming the state-of-the-art methods with lower computational cost.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9605-9621"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2024 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 17","authors":"","doi":"10.1109/JSTARS.2025.3553722","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3553722","url":null,"abstract":"","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"20355-20637"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photometric Modeling and Correction of the JiLin-1 Lunar Observations Using the Hapke Model","authors":"Tian-Yi Xu;Min Shu;Yunzhao Wu","doi":"10.1109/JSTARS.2025.3554240","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554240","url":null,"abstract":"The PMS2 spectral imager onboard JiLin-1 (JL-1) satellite conducted extensive lunar observations, capturing images of the Moon across multiple spectral bands ranging from 415 to 1012 nm, spanning a wide range of lunar phases over an extended period. Photometric modeling plays a key role in deriving the physical and photometric properties of the lunar surface, with photometric calibration serving as a critical step in enhancing data quality for subsequent analyses. In this study, the PMS2 data are used for disk-integrated photometric modeling across these bands with the Hapke model. We analyze the wavelength dependence of photometric parameters and assess the effectiveness of photometric correction. Our study reveals a linear relationship between the amplitude of the opposition effect and wavelength, and gives the functional relationship between the other parameters and wavelength. Notably, we identified for the first time a peak in backscattering near 650 nm for the lunar surface. In addition, we demonstrate that the model parameters derived from a disk-integrated approach provide accurate results when applied to disk-resolved photometric calibration.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9333-9339"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CD4C: Change Detection for Remote Sensing Image Change Captioning","authors":"Xiliang Li;Bin Sun;Zhenhua Wu;Shutao Li;Hu Guo","doi":"10.1109/JSTARS.2025.3554385","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554385","url":null,"abstract":"Remote sensing image change captioning is an important image interpretation technique that automatically generates captions describing the visual changes in multitemporal remote sensing images. However, the visual changes present in multitemporal images can be classified as foreground changes, which are captured in captions, and background changes, which interfere with traditional methods and complicate the effective capture of foreground changes. This ultimately limits the overall performance of the model. To address this issue, this study introduces change detection for remote sensing image change captioning (CD4C). Specifically, a change detection module generates binary masks that contain relevant visual change information from multitemporal images. Subsequently, based on whether changes are detected, samples are classified and processed through the C-Stream and N-Stream of the multitemporal difference feature fusion (MDF) module to extract visual change features. The C-Stream leverages the visual change information provided by the mask to enhance the ability of CD4C to capture foreground visual change features at both the image and feature levels. The N-Stream incorporates a pseudofeature generation module designed to mitigate the interference caused by poor change detection results. Finally, the caption generation module interprets the visual change features extracted by the MDF to produce accurate textual descriptions. Experiments on the LEVIR-CC and Dubai-CC datasets demonstrate that the proposed method outperforms other approaches.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9181-9194"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Signal Compensation of Moon Mineralogy Mapper (M3) Under Low-Illumination Conditions Using a CycleGAN-Based Network","authors":"Rui Ni;Fei Zhao;Tingyu Meng;Yanlei Du;Pingping Lu;Robert Wang","doi":"10.1109/JSTARS.2025.3549768","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3549768","url":null,"abstract":"Lunar south polar regions have attracted considerable scientific interest due to their potential for preservation of water ice and unique mineralogical compositions. As a key scientific payload for surface composition exploration missions, hyperspectral imager faces significant challenges in the lunar polar regions. The primary issue is the low-illumination conditions in these areas, where terrain-induced shadows drastically reduce the signal-to-noise ratio (SNR) of hyperspectral images (HSIs), resulting in limited availability of reliable spectral available for polar region analysis. Previous studies have largely bypassed low-SNR spectra or filtered them out, as there has been no effective method to recover the spectral information under these harsh conditions. To tackle this problem, an effective method based on CycleGAN network is proposed to compensate hyperspectral data obtained by Moon mineralogy mapper (M3) under low-illumination conditions in lunar south polar regions. The network was trained by constructing paired datasets of low and high SNR M3 spectra from the lunar South Pole. The efficacy of the proposed method is validated using real high SNR M3 spectral observations, with the performance of the compensated results comprehensively assessed across three dimensions: structural indicators, spectral indices, and spatial consistency analysis. The strong correlation between the M3 spectral compensation results with Selenological Engineering Explorer (Kaguya) multiband imager data, as well as other sensors' inversion of plagioclase abundance around the Shackleton Crater, underscores the network's potential for mineral exploration. To the best of authors' knowledge, this study represents one of the first efforts to compensate illumination-limited spectra in lunar HSI. It provides an efficient method for enhancing the SNR of M3 spectra in the lunar polar region, offering a reliable tool and novel insights for future mineralogical and water ice studies.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8504-8522"},"PeriodicalIF":4.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10936990","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EMCFormer: Equalized Multimodal Cues Fusion Transformer for Remote Sensing Visible-Infrared Object Detection Under Long-Tailed Distribution","authors":"Zian Wang;Xianghui Liao;Jin Yuan;Chen Lu;Zhiyong Li","doi":"10.1109/JSTARS.2025.3553747","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3553747","url":null,"abstract":"Visible-infrared object detection has been widely applied in multimodal remote sensing image perception tasks due to the strong complementarity between the two modalities. However, visible-infrared remote sensing data often exhibits long-tail distribution characteristics, where some categories have sparse samples, resulting in insufficient training and poor detection performance for tail categories. To address this issue, this paper proposes an “Equalized Multi-modal Cues Fusion Transformer” (EMCFormer), incorporating an innovative “Multi-modal Heterogeneous Cues Aggregation” (MHCA) module and “Equalized-Adaptive Focal Loss” (EAFL). Specifically, MHCA leverages a cross-modal self-attention mechanism with Gumbel Softmax to generate fused features and enhance the learning of tail category features. By introducing Gumbel random noise, MHCA effectively increases attention on sparse data from tail categories, thereby producing robust fused features that enhance detection performance for these categories. In addition, EAFL dynamically amplifies the contribution of tail categories by using a dynamic focusing factor, improving performance for tail category detection. Extensive experiments on well-recognized datasets demonstrate that EMCFormer effectively improves detection accuracy for tail categories and mitigates the challenges posed by long-tail data distribution.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9533-9545"},"PeriodicalIF":4.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of Paddy Rice Planting Area Through Feature Selection Method Using Sentinel-1/2 Time Series Images","authors":"Shiyu Zhang;Pengao Li;Yong Xie;Wen Shao;Xueru Tian","doi":"10.1109/JSTARS.2025.3552589","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3552589","url":null,"abstract":"Utilizing remote sensing technology to accurately and efficiently extract paddy rice planting area is crucial for ensuring food security. In southern Jiangsu, cloudy and rainy weather impairs the effectiveness of optical satellite images, while complex surface coverage reduces the precision of paddy rice classification. Therefore, this study took Liyang City as the study area, reconstructed Sentinel-2 cloud-free time series optical images, and extracted spectral features, vegetation indexes, and other features, in combination with the polarization features of the Sentinel-1 time series radar images. The optimal feature subset was selected through the feature selection method, and machine learning algorithms were optimized for paddy rice planting area classification. Results indicated that: 1) The reconstruction of cloud-free time series images with the Cloud Score+ method and the integrated NSPI and MNSPI approach was stable and effective, with correlation coefficients (<italic>r</i>) exceeding 0.87 and low values for indicators such as root mean square error (RMSE), Robert's edge (Edges), and local binary patterns (LBP), meeting the requirements for paddy rice classification. 2) The classification accuracy of combining Sentinel-1 polarization features with Sentinel-2 spectral features could improve by up to 10.52% compared to before the combination. The combination of polarization features, spectral features, and difference features achieved the highest overall accuracy (OA), but the mapping exhibited salt-and-pepper noise. 3) The integration of multi-source remote sensing data and feature selection effectively improved paddy rice classification accuracy. The correlation-based feature selection and greedy step wise algorithms performed the best, with an OA of 93.97% and a Kappa coefficient (Kappa) of 0.9176, producing less mapping noise and higher classification accuracy for paddy rice. The study provides methodological support and a practical case for paddy rice planting area classification in the southern region using remote sensing.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8747-8762"},"PeriodicalIF":4.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10934737","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Geoscience and Remote Sensing Society Information for Authors","authors":"","doi":"10.1109/JSTARS.2025.3553720","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3553720","url":null,"abstract":"","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"C3-C3"},"PeriodicalIF":4.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Resolution Sea Ice Concentration Using a Sentinel-1 U-Net Ice-Water Classifier","authors":"Jozef Rusin;Anthony P. Doulgeris;K. Andrea Scott;Thomas Lavergne;Catherine Taelman","doi":"10.1109/JSTARS.2025.3553623","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3553623","url":null,"abstract":"Accurate and high-resolution sea ice concentration (SIC) mapping is essential for polar navigation, environmental monitoring, and assimilation into forecast models. Traditional passive microwave sensors, such as AMSR2, provide reliable SIC estimates but are limited by coarse (5 km) resolution, particularly near coastlines and regions with mixed ice and open water, where finer spatial detail is critical. Synthetic aperture radar (SAR) imagery offers a high-resolution alternative. This study applies a U-Net convolutional neural network to Sentinel-1 SAR data, utilizing pixelwise ice-water labels to enhance SIC mapping. To address SAR noise challenges, we incorporate multilooking, adaptive noise correction, and overlapping patches at inference to improve SIC accuracy while preserving fine-scale features. We trained the U-Net across multilooking levels to balance resolution, noise reduction, and computational efficiency, allowing the model to handle noise artefacts effectively. Our results identify an optimal 7 × 7 multilooking level, achieving 280 m ice-water labels and a 2.5 km SIC field when an additional 9 × 9 SIC window is applied. This configuration enhances traditional SIC products by improving the representation of the ice edge, leads, and near-coastal features, which are critical for operational applications. SAR-derived SIC addresses the limitations of passive microwave products by providing superior spatial detail and ice edge resolution. Incorporating additional information from AMSR2 or wind features could strengthen SIC robustness and minimize the misclassification of open water, which is present in the results. These advancements would establish SAR-based SIC as a valuable tool for operational sea ice monitoring and integration into high-resolution models.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9380-9395"},"PeriodicalIF":4.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10936980","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote Sensing Change Detection With Forward–Backward Diffusion and Multidirectional Scanning","authors":"Hexin Yuan;Peng Wang;Haibo Wang;Cui Ni;Yali Liu;Chao Ma","doi":"10.1109/JSTARS.2025.3553206","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3553206","url":null,"abstract":"With the development and research of remote sensing change detection methods, remote sensing change detection combined with deep learning has achieved excellent results. However, the existing techniques still struggle to achieve the accurate detection outcomes when confronted with challenges, such as low image resolution and noise. In addition, a significant issue remains in the detection of large continuous change regions, which often leads to leakage problems. In this article, we introduce a novel approach, diffusion scanning change detection, which integrates forward and backward diffusion processes with multidirectional scanning techniques. The input image is first preprocessed using a forward diffusion process. The backward diffusion process, along with a state-space model that incorporates multidirectional scanning, is subsequently employed during feature extraction to mitigate the adverse effects of low resolution and noise on detection accuracy. Finally, the multidirectional scanning strategy, which is enhanced by an attention mechanism, is applied in the decoder to address the leakage problem associated with large continuous change regions. The experimental results demonstrate that the proposed method significantly outperforms the existing change detection methods, as evidenced by improved performance metrics, including the overall accuracy, intersection over union, and <italic>F</i>1-score.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8763-8776"},"PeriodicalIF":4.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10935675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}