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

{"title":"STFCropNet: A Spatiotemporal Fusion Network for Crop Classification in Multiresolution Remote Sensing Images","authors":"Wei Wu;Yapeng Liu;Kun Li;Haiping Yang;Liao Yang;Zuohui Chen","doi":"10.1109/JSTARS.2025.3531886","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3531886","url":null,"abstract":"Remote sensing-based classification of crops is the foundation for the monitoring of food production and management. A range of remote sensing images, encompassing spatial, spectral, and temporal dimensions, has facilitated the classification of crops. However, prevailing methods for crop classification via remote sensing focus on either temporal or spatial features of images. These unimodal methods often encounter challenges posed by noise interference in real-world scenarios, and may struggle to discriminate between crops with similar spectral signatures, thereby leading to misclassification over extensive areas. To address the issue, we propose a novel approach termed spatiotemporal fusion-based crop classification network (STFCropNet), which integrates high-resolution (HR) images with medium-resolution time-series (TS) images. STFCropNet consists of a temporal branch, which captures seasonal spectral variations and coarse-grained spatial information from TS data, and a spatial branch that extracts geometric details and multiscale spatial features from HR images. By integrating features from both branches, STFCropNet achieves fine-grained crop classification while effectively reducing salt and pepper noise. We evaluate STFCropNet in two study areas of China with diverse topographic features. Experimental results demonstrate that STFCropNet outperforms state-of-the-art models in both study areas. STFCropNet achieves an overall accuracy of 83.2% and 90.6%, representing improvements of 3.6% and 4.1%, respectively, compared to the second-best baseline model. We release our code at.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4736-4750"},"PeriodicalIF":4.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10848201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361111","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":"Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining Region","authors":"Shuang Wu;Lei Deng;Qinghua Qiao","doi":"10.1109/JSTARS.2025.3531439","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3531439","url":null,"abstract":"Accurate long-term estimation of fractional vegetation cover (FVC) is crucial for monitoring vegetation dynamics. Satellite-based methods, such as the dimidiate pixel method (DPM), struggle with spatial heterogeneity due to coarse resolution. Existing methods using unmanned aerial vehicles (UAVs) combined with satellite data (UCS) inadequately leverage the high spatial resolution of UAV imagery to address spatial heterogeneity and are seldom applied to long-term FVC monitoring. To overcome spatial challenges, an improved dimidiate pixel method (IDPM) is proposed here, utilizing 2021 Landsat imagery to generate FVC_DPM via DPM and upscaled UAV imagery for FVC_UAV as ground references. The IDPM uses the pruned exact linear time method to segment the normalized difference vegetation index (NDVI) into intervals, within which DPM performance is evaluated for potential improvements. Specifically, if the difference (D) between FVC_DPM and FVC_UAV is nonzero, NDVI-derived texture features are incorporated into FVC_DPM through multiple linear regression to enhance accuracy. To address temporal challenges and ensure consistency across years, the 2021 NDVI serves as a reference for inter-year NDVI calibration, employing least squares regression (LSR) and histogram matching (HM) to identify the most effective method for extending the IDPM to other years. Results demonstrate that 1) the IDPM, by developing distinct DPM improvement models for different NDVI intervals, considerably improves UAV and satellite data integration, with a 48.51% increase in <italic>R</i>² and a 56.47% reduction in root mean square error (RMSE) compared to the DPM and UCS and 2) HM is found to be more suitable for mining areas, increasing <italic>R</i>² by 25.00% and reducing RMSE by 54.05% compared to LSR. This method provides an efficient, rapid solution for mitigating spatial heterogeneity and advancing long-term FVC estimation.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4162-4173"},"PeriodicalIF":4.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10845181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105990","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":"Deep Learning and Wavelet Transform Combined With Multichannel Satellite Images for Tropical Cyclone Intensity Estimation","authors":"Chang-Jiang Zhang;Mei-Shu Chen;Lei-Ming Ma;Xiao-Qin Lu","doi":"10.1109/JSTARS.2025.3531448","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3531448","url":null,"abstract":"Tropical cyclone (TC) is a highly catastrophic weather event, and accurate estimation of intensity is of great significance. The current proposed TC intensity estimation model focuses on training using satellite images from single or two channels, and the model cannot fully capture features related to TC intensity, resulting in low accuracy. To this end, we propose a double-layer encoder–decoder model for estimating the intensity of TC, which is trained using images from three channels: infrared, water vapor, and passive microwave. The model mainly consists of three modules: wavelet transform enhancement module, multichannel satellite image fusion module, and TC intensity estimation module, which are used to extract high-frequency information from the source image, generate a three-channel fused image, and perform TC intensity estimation. To validate the performance of our model, we conducted extensive experiments on the TCIR dataset. The experimental results show that the proposed model has MAE and RMSE of 3.76 m/s and 4.62 m/s for TC intensity estimation, which are 15.70% and 20.07% lower than advanced Dvorak technology, respectively. Therefore, the model proposed in this article has great potential in accurately estimating TC intensity.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4711-4735"},"PeriodicalIF":4.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10845190","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361110","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":"Unsupervised Image Super-Resolution for High-Resolution Satellite Imagery via Omnidirectional Real-to-Synthetic Domain Translation","authors":"Minkyung Chung;Yongil Kim","doi":"10.1109/JSTARS.2025.3530959","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3530959","url":null,"abstract":"Image super-resolution (SR) aims to enhance the spatial resolution of images and overcome the hardware limitations of imaging systems. While deep-learning networks have significantly improved SR performance, obtaining paired low-resolution (LR) and high-resolution (HR) images for supervised learning remains challenging in real-world scenarios. In this article, we propose a novel unsupervised image super-resolution model for real-world remote sensing images, specifically focusing on HR satellite imagery. Our model, the bicubic-downsampled LR image-guided generative adversarial network for unsupervised learning (BLG-GAN-U), divides the SR process into two stages: LR image domain translation and image super-resolution. To implement this division, the model integrates omnidirectional real-to-synthetic domain translation with training strategies such as frequency separation and guided filtering. The model was evaluated through comparative analyses and ablation studies using real-world LR–HR datasets from WorldView-3 HR satellite imagery. The experimental results demonstrate that BLG-GAN-U effectively generates high-quality SR images with excellent perceptual quality and reasonable image fidelity, even with a relatively smaller network capacity.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4427-4445"},"PeriodicalIF":4.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10844307","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106186","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":"A Two-Stage Method for Screening Pulse RFI in SAR Raw Data Alternating the Use of Time and Frequency Domains","authors":"Tingting Wei;Xingwang Hu;Zhengwei Guo;Gaofeng Shu;Yabo Huang;Ning Li","doi":"10.1109/JSTARS.2025.3530989","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3530989","url":null,"abstract":"In the increasingly complex electromagnetic environment, the spectrum is becoming more and more crowded. Synthetic aperture radar (SAR) is more susceptible to be affected by the radio frequency interference (RFI) in the same frequency band when receiving echo signal. Pulse RFI (PRFI) is a common form of RFI and often has time-varying characteristics, which will deteriorate the SAR images quality and hinder image interpretation. To effectively suppress the PRFI, the serial number of the pulses in SAR raw data containing PRFI need to be screened out with high precision. A two-stage method for screening PRFI in SAR raw data alternating the use of time and frequency domains was proposed in this article. First, range-cell level difference screening is performed in the time domain and frequency domain, respectively, to initially screen the PRFI. Then, the preliminary screening results are accumulated along the range direction, and the accumulated results are classified using a clustering algorithm to perform pulse-level screening to obtain the serial number of the pulses containing PRFI. Compared with the traditional PRFI screening methods, the proposed approach boasts a remarkable ability to circumvent missed screening and false alarm when screening weak-energy PRFIs. It possesses exceptional sensitivity and accuracy, offering fresh perspectives and innovative solutions to the PRFI screening challenge. The effectiveness and superiority of the proposed method are verified by the simulation data and measured data experiments.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4331-4346"},"PeriodicalIF":4.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10844320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105432","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":"DIMD-DETR: DDQ-DETR With Improved Metric Space for End-to-End Object Detector on Remote Sensing Aircrafts","authors":"Huan Liu;Xuefeng Ren;Yang Gan;Yongming Chen;Ping Lin","doi":"10.1109/JSTARS.2025.3530141","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3530141","url":null,"abstract":"Aircraft target detection in remote sensing images faces numerous challenges, including target size variations, low resolution, and complex backgrounds. To address these challenges, an enhanced end-to-end aircraft detection framework (DIMD-DETR) is developed based on an improved metric space. Initially, a bilayer targeted prediction method is proposed to strengthen gradient interaction across decoder layers, thereby enhancing detection accuracy and sensitivity in complex scenarios. The pyramid structure and self-attention mechanism from pyramid vision transformer V2 are incorporated to enable effective joint learning of both global and local features, which significantly boosts performance for low-resolution targets. To further enhance the model's generalization capabilities, an aircraft-specific data augmentation strategy is meticulously devised, thereby improving the model's adaptability to variations in scale and appearance. In addition, a metric-space-based loss function is developed to optimize the collaborative effects of the modular architecture, enhancing detection performance in complex backgrounds and under varying target conditions. Finally, a dynamic learning rate scheduling strategy is proposed to balance rapid convergence with global exploration, thereby elevating the model's robustness in challenging environments. Compared to current popular networks, our model demonstrated superior detection performance with fewer parameters.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4498-4509"},"PeriodicalIF":4.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843752","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105957","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":"PCCN: Polarimetric Contexture Convolutional Network for PolSAR Image Super-Resolution","authors":"Lin-Yu Dai;Ming-Dian Li;Si-Wei Chen","doi":"10.1109/JSTARS.2025.3530136","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3530136","url":null,"abstract":"Polarimetric synthetic aperture radar (PolSAR) can acquire full-polarization information, which is the solid foundation for target scattering mechanism interpretation and utilization. Meanwhile, PolSAR image resolution is usually lower than the synthetic aperture radar (SAR) image, which may limit its potentials for target detection and recognition. Image super-resolution with the convolutional neural network is a promising solution to fulfill this issue. In order to make full use of both polarimetric and spatial information to further enhance super-resolution performance, this work proposes the polarimetric contexture convolutional network (PCCN) for PolSAR image super-resolution. The main contributions are threefold. First, a new PolSAR data representation of the polarimetric contexture matrix is established, which can fully represent the cube of polarimetric and spatial information into a coded matrix. Then, a dual-branch architecture of the polarimetric and spatial feature extraction block is designed to extract both polarimetric and spatial features separately. Finally, these intrinsic polarimetric and spatial features are effectively fused at both local and global levels for PolSAR image super-resolution. The proposed PCCN method is trained with one <italic>X</i>-band polarimetric and interferometric synthetic aperture radar (PiSAR) data, while evaluated with the same scene but different PiSAR imaging direction and with different sensors data including the <italic>C</i>-band Radarsat-2 and the <italic>X</i>-band COSMO-SkyMed of various imaging scenes. Compared with state-of-the-art algorithms, experimental studies demonstrate and validate the effectiveness and superiority of the proposed method in both visualization examination and quantitative metrics. The proposed method can provide better super-resolution PolSAR images from both polarimetric and spatial viewpoints.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4664-4679"},"PeriodicalIF":4.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361106","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":"Soil Surface Roughness in Temperate Forest During SMAPVEX19-22","authors":"Victoria A. Walker;Michael H. Cosh;William A. White;Andreas Colliander;Victoria R. Kelly;Paul Siqueira","doi":"10.1109/JSTARS.2025.3530710","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3530710","url":null,"abstract":"Data were collected across multiple forested domains during the Soil Moisture Active Passive Validation Experiment 2019–2022 to improve understanding of soil moisture retrievals under dense vegetation. Soil surface roughness was one of many soil and vegetation parameters sampled during intensive operations periods during the spring and summer of 2022 because of its importance to retrieval accuracy (rougher soils have a higher emissivity and reduced sensitivity to soil moisture compared to smooth soils with otherwise identical characteristics). A total of 410 valid pinboard transects were collected across 24 sites between the two temperate forest domains located in the northeastern United States. Two experimental methods (handheld lidar and ultrasonic robot) were additionally tested at select sites. After removal of topographic slope, the forest floor was found to be relatively smooth with average rms heights of <inline-formula><tex-math>$9+-1 ,{mathrm{mm}}$</tex-math></inline-formula> in the central Massachusetts domain and <inline-formula><tex-math>$6+-1 ,{mathrm{mm}}$</tex-math></inline-formula> in the Millbrook, New York domain. These correspond to estimates of the model roughness parameter, <inline-formula><tex-math>$h$</tex-math></inline-formula>, of 0.31 and 0.16, respectively, which is within the range of accepted lookup table values but smoother than suggested by recent studies retrieving <inline-formula><tex-math>$h$</tex-math></inline-formula> over forests.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4640-4647"},"PeriodicalIF":4.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106082","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":"Two-Level Semantic-Driven Diffusion Based Hyperspectral Pansharpening","authors":"Lin He;Wenrui Liang;Antonio Plaza","doi":"10.1109/JSTARS.2025.3529993","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3529993","url":null,"abstract":"Over recent years, denoising diffusion probabilistic models (DDPMs) have received many attentions due to their powerful ability to infer data distribution. However, most of existing DDPM-based hyperspectral (HS) pansharpening methods over rely on local processing to perform recovery, which usually fails to reconcile global contextual semantics and local details in data. To address the issue, we propose a two-level semantic-driven diffusion method for HS pansharpening. In our method, we first extract semantics in two levels, where the low-level semantic not only leads the extraction of conditional details, but also supports the further semantic extraction while the high-level semantic is related to scene cognition. Then, the features from both the low-level and high-level semantics are conditionally injected to the denoising network to guide the high-resolution HS recovery. Experiments on multiple datasets verify the effectiveness of our method.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4213-4226"},"PeriodicalIF":4.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10842049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106033","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":"Assessing the Impact of Waterfront Environments on Public Well-Being Through Digital Twin Technology","authors":"Junjie Luo;Zheng Yuan;Lingzi Xu;Wenhui Xu","doi":"10.1109/JSTARS.2025.3530762","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3530762","url":null,"abstract":"The application of digital twin (DT) technology in studying public environmental perception and associated health benefits is emerging, yet most research has focused on static green spaces, providing limited insights into dynamic waterscapes. This study aims to systematically evaluate the effects of waterfront and nonwaterfront environments on public physiological and psychological responses using a DT platform. A high-precision 3-D virtual replica of a suburban park was constructed using UAV oblique photogrammetry and handheld lidar scanning technologies. Real-time environmental data were integrated into the DT using IoT devices, establishing a dynamic link between the digital environment and physical worlds. Participants underwent field tests in both environments, measuring physiological indicators (e.g., heart rate and blood oxygen saturation) and psychological indicators (e.g., pleasure and relaxation). We found that waterfront environments outperformed nonwaterfront environments in terms of relaxation and vitality, while no significant differences were observed between the two environments regarding physiological indicators. In addition, ANCOVA and random forest analyses identified temperature and sunlight intensity as key environmental factors influencing heart rate and psychological well-being. The study reveals specific mechanisms through which different environmental characteristics impact public well-being and demonstrates the DT platform's capabilities in real-time environmental data collection and landscape quantification. These findings provide valuable insights for urban planners and public health policymakers in designing landscapes that enhance urban residents' health and well-being.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4536-4553"},"PeriodicalIF":4.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106041","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}