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

{"title":"A Kriging Interpolation-Enhanced MART for Nonuniform Observational Data in Geosynchronous SAR-Based Computerized Ionospheric Tomography","authors":"Yi Sui;Xichao Dong;Yuanhao Li;Zhiyang Chen;Cheng Hu","doi":"10.1109/JSTARS.2025.3542074","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542074","url":null,"abstract":"The ionosphere affects spaceborne synthetic aperture radar (SAR) and Global Navigation Satellite System (GNSS) missions, and reflects solar activity and Earth magnetic field, making its monitoring essential. Computerized ionospheric tomography (CIT) is a key technique for this application, using total electron content (TEC) along signal paths to reconstruct electron density. Unlike GNSS-based CIT using ground receivers, and low Earth orbit (LEO) SAR-based CIT limited by coverage and revisit time, geosynchronous (GEO) SAR-based CIT directly reconstructs ionospheric electron density using TEC from ground permanent scatterer (PS) points. The high orbit, wide coverage, and short revisit time of GEO SAR enable observation of the full vertical ionosphere structure and larger areas with better time resolution. However, GEO SAR-based CIT faces challenges due to the nonuniform distribution of PS points, leading to significant spatial differences in tomographic quality, making it difficult to provide reliable data support for subsequent research. To address this, a Kriging interpolation-enhanced multiplicative algebraic reconstruction technique (MART) is proposed. This proposed method embeds Kriging interpolation into the conventional MART iteration, utilizing the spatial correlation of electron density to compensate for voxels with sparse TEC data, while still incorporating the voxel's own TEC data. This improves the electron density reconstruction accuracy and robustness. Finally, both simulation and real GNSS data experiments demonstrate that the proposed method significantly improves reconstruction in data-sparse voxels and reduces spatial differences in electron density estimation quality compared to conventional method, albeit with a slight tradeoff in accuracy for a small number of voxels.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6600-6616"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553367","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":"An Adaptive Spherical Simplex Radial Cubature Information Filter-Based Phase Unwrapping Method","authors":"Jia Jinguo;Liu Fang;Huang Qingnan;Xie Xianming","doi":"10.1109/JSTARS.2025.3542608","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542608","url":null,"abstract":"An adaptive spherical simplex radial cubature information filter-based phase unwrapping (ASSRCIFPU) method is introduced. First, the ASSRCIF method with adaptive adjustment of observation noise variance is introduced within the phase unwrapping for interferograms. The ASSRCIFPU program is implemented by integrating a rapid local phase gradient estimator with a heap sort path-following technique. Second, a deep learning-based interferogram fringe boundary detection model is developed to extract fringe boundary information for the interferograms. Subsequently, the fringe boundary information, the pseudocoherence coefficient map and the residue data from the interferogram are combined to produce a reliability mask map that characterizes the phase quality for the interferograms, which categorize the pixels into high-reliability and low-reliability groups based on the phase quality. Finally, the ASSRCIFPU program first unwraps the high-reliability pixel arrays using heap sort path-following technique, followed by unwrapping the remaining wrapped pixels to retrieve the unwrapped phase of the entire interferogram. Experiments on diverse fringe patterns show that this method achieves higher accuracy and efficiency compared to other commonly used methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6668-6680"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10890981","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594446","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 Novel Approach for High-Resolution Coastal Areas and Land Use Recognition From Remote Sensing Images Based on Multimodal Network-Level Fusion of SRAN3 and Lightweight Four Encoders ViT","authors":"Muhammad Kashif Bhatti;Muhammad Attique Khan;Saima Shaheen;Ameer Hamza;Ali Arishi;Dina Abdulaziz AlHammadi;Shabbab Ali Algamdi;Yunyoung Nam","doi":"10.1109/JSTARS.2025.3542194","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542194","url":null,"abstract":"Land use land cover classification from satellite images (remote sensing) has shown many efforts from the last decade due to ecological surveillance, rapid urbanization, law enforcement, climate change, agriculture drought, and disaster recovery. The low-resolution remote sensing images impact on the accurate prediction; therefore, the high-resolution deep learning architecture is widely required. This article proposes a new deep network-level fusion approach that merges a stacked residual self-attention CNN (SRAN3) with a lightweight ViT based on 4-encoders to enhance the model performance while reducing computational costs. The SRAN3 model is proposed for extracting sophisticated prominent features, while the 4-encoder-based ViT facilitates effective learning with reduced computation time. These networks are fused using a depth concatenation approach that effectively integrates the strengths of both architectures. The fused model hyperparameters are selected through Bayesian optimization, significantly improving the learning process. The trained model is later utilized in the testing phase, extracting features from the depth-concatenation layer. The extracted features are fed to neural network classifiers and obtain the final prediction. Two publicly available datasets, EuroSAT and NWPU_RESIS45, are employed to obtain improved testing and validation accuracy. The proposed SRAN3 + WNN (Wide Neural Network) and 4-encoder ViT + WNN obtained 96.9% and 92.6% of accuracy; however, the proposed fused network + WNN achieved the highest accuracy of 98.4% on EuroSAT and 94.7% accuracy on the NWPU_RESIS45 dataset, respectively. Also, the proposed fused model interpretation is performed using the explainable artificial technique (XAI), which has shown improved land use and land cover classification.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6844-6858"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601861","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":"Semantic Segmentation of High-Resolution Remote Sensing Imagery via an End-to-End Graph Attention Network With Superpixel Embedding","authors":"Ying Tang;Xiangyun Hu;Tao Ke;Mi Zhang","doi":"10.1109/JSTARS.2025.3542255","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542255","url":null,"abstract":"Semantic segmentation of high-resolution remote sensing images is crucial in ecological evaluation, natural resource surveys, etc. Compared with CNN-based and transformer-based methods, graph neural networks (GNNs) have drawn increasing attention because they can flexibly model topologies of arbitrary irregular objects on graphs. Researchers typically use superpixels as graph nodes to reduce image noise and computational complexity. However, most superpixel-based GNN methods view superpixel segmentation as a data preprocessing step. This results in fixed graphs input to GNNs and overlooks the effects of undersegmentation. In addition, these methods often employ one graph construction approach, which makes them susceptible to interclass similarity (ICS) or intraclass variability (ICV), leading to segmentation inaccuracies. To address these issues, we propose an end-to-end graph attention network with superpixel embedding (SEGAT) to achieve semantic segmentation with well-delineated boundaries. We first use a learnable neural network, the superpixel generation module (SGM), to generate superpixels, which is cotrained with subsequent graph segmentation module (GSM) to refine boundaries continuously. Dynamically fine superpixels produce dynamically optimized graphs and mitigate undersegmentation errors. To reduce the interference of ICS and ICV, we then use the GSM to construct local and global graphs based on superpixel spatial positions and feature similarity, respectively, and update superpixel features and graph structure. Finally, updated superpixel features are classified for superpixel-wise classification, which is then mapped back to pixel features through the pixel-superpixel association map. Extensive experiments on three datasets, Vaihingen, Potsdam, and UAVid, demonstrate that SEGAT can outperform state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7236-7252"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637900","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":"FDC-TA-DSN Ship Classification Model and Dataset Construction Based on Complex-Valued SAR","authors":"Gui Gao;Yucong He;Jinghao Zhao;Sijie Li;Meixiang Wang;Gang Yang;Xi Zhang","doi":"10.1109/JSTARS.2025.3542436","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542436","url":null,"abstract":"Synthetic aperture radar (SAR) ship classification is of great significance in the field of maritime observation. On one hand, how to comprehensively utilize the amplitude and phase information in SAR data has become a key problem for improving the performance of ship classification. On the other hand, there is a lack of available complex-valued SAR databases for the purpose of classification. To solve the above problems, a complex-valued SAR deep learning model, FDC-TA-DSN, based on four-dimensional dynamic convolution (FDC) and triple attention (TA) mechanism, is proposed. First, this new deep SAR-Net (DSN) devises an FDC module to reduce the influence of SAR speckle noise and enhance the adaptability of the network for inputting features, and a TA module to suppress background sea clutter and capture important features. Second, joint time-frequency analysis was used to obtain the radar spectrogram of SAR data, and the stacked convolutional autoencoder was used to learn the phase information of SAR data to obtain the backscattering characteristics. Finally, the two kinds of information are formed into fusion features for learning to improve the classification accuracy. To support this investigation, a complex-valued SAR dataset ComplexSAR_Ship is constructed for the first time by using the two high-resolution modes of UFS and FSI of the Gaofen-3 satellite. The dataset includes 17 ship types with nearly 3000 high-resolution ship slices. The experimental results show that, compared with the current popular networks, such as DSN, ResNet, VGG, etc., FDC-TA-DSN has achieved better performance, and the network has good generalization ability in SAR data classification.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7034-7047"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10890957","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706568","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":"Pixel-Level and Global Similarity-Based Adversarial Autoencoder Network for Hyperspectral Unmixing","authors":"Wei Tao;Haiyang Zhang;Shan Zeng;Long Wang;Chaoxian Liu;Bing Li","doi":"10.1109/JSTARS.2025.3542228","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542228","url":null,"abstract":"Hyperspectral unmixing is a critical task in remote sensing, enabling the decomposition of hyperspectral data into their constituent endmembers and abundances. The loss of the traditional unmixing algorithm based on deep learning typically depends on reducing the discrepancy between the original and reconstructed hyperspectral image. However, during the training process, the loss feedback method is relatively simple, resulting highly random unmixing results. Moreover, spatial feature extraction can effectively improve the unmixing effect, but existing spatial feature extraction methods in hyperspectral unmixing still have significant room for improvement. To address these challenges, we propose a novel adversarial autoencoder unmixing network considering pixel-level and global similarity measurements based on a Wasserstein generative adversarial network (WGAN) and a U-shaped transformer-enhanced architecture. The WGAN ensures stable gradient updates through a gradient penalty, maintaining Lipschitz continuity, while the U-shaped network with Swin transformer blocks captures both local and global spatial features. Experiments were conducted on synthetic and real-world hyperspectral datasets. Our method outperformed state-of-the-art approaches, achieving improvement in root mean square error and spectral angle distance (SAD). The SAD is a metric that quantifies the angular difference between the true and estimated endmember spectra, our method improves the mean SAD by at least 8.7% compared to competing algorithms, representing an enhancement in unmixing performance. Notably, the method demonstrated superior robustness in low signal-to-noise ratio scenarios, maintaining high unmixing accuracy. These results highlight the potential of our approach to advance unmixing research by addressing both pixel-level and global similarity constraints, providing a new way for hyperspectral unmixing.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7064-7082"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887325","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621930","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":"Enabling Advanced Land Cover Analytics: An Integrated Data Extraction Pipeline for Predictive Modeling With the Dynamic World Dataset","authors":"Victor Radermecker;Andrea Zanon;Nancy Thomas;Annita Vapsi;Saba Rahimi;Rama Ramakrishnan;Daniel Borrajo","doi":"10.1109/JSTARS.2025.3542282","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3542282","url":null,"abstract":"Understanding land cover holds considerable potential for a myriad of practical applications, particularly as data accessibility transitions from being exclusive to governmental and commercial entities to now including the broader research community. Nevertheless, although the data is accessible to any community member interested in exploration, there exists a formidable learning curve and no standardized process for accessing, preprocessing, and leveraging the data for subsequent tasks. In this study, we democratize this data by presenting a flexible and efficient end-to-end pipeline for working with the Dynamic World dataset, a cutting-edge near-real-time land use/land cover (LULC) dataset. This includes a preprocessing and representation framework which tackles noise removal, efficient extraction of large amounts of data, and re-representation of LULC data in a format well suited for several downstream tasks. To demonstrate the power of our pipeline, we use it to extract data for an urbanization prediction problem and build a suite of machine learning models with excellent performance. This task is easily generalizable to the prediction of any type of land cover and our pipeline is also compatible with a series of other downstream tasks. To facilitate further research and validation, all code and data used in this study are made available online.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6440-6450"},"PeriodicalIF":4.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553133","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":"Early Forest Fire Detection With UAV Image Fusion: A Novel Deep Learning Method Using Visible and Infrared Sensors","authors":"Kunlong Niu;Chongyang Wang;Jianhui Xu;Jianrong Liang;Xia Zhou;Kaixiang Wen;Minjian Lu;Chuanxun Yang","doi":"10.1109/JSTARS.2025.3541205","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3541205","url":null,"abstract":"Global warming has significantly increased the frequency of forest fires. Unmanned aerial vehicles (UAVs) provide rapid response and real-time monitoring, offering unique advantages over traditional human inspections and satellite monitoring. Their ability to monitor large forest areas during the early stages of fires supports timely warning. UAVs typically detect fires by capturing visible and infrared images. Visible images are effective for smoke detection but are influenced by environmental factors, while infrared images are better at detecting heat but can misidentify fires when the temperature difference between the fire and its surroundings is minimal. Additionally, challenges in image registration often occur when aligning the two image types for fusion. Therefore, this research proposes a novel method to early forest fire detection by fusing visible and infrared images and creating a dataset. The main contributions include: 1) the creation of a dataset containing 2752 synchronized visible and infrared image pairs to overcome existing dataset limitations; 2) the application of deep learning techniques to enhance image registration and fusion, incorporating an improved algorithm that increases automation; and 3) the development of the Forest Fire Detection Model—Fusion (FFDM-F) model, based on YOLOv5s and fused images, designed to accurately detect small fires at their early stages. The results show that the improved registration method effectively aligns visible and infrared images, optimizing the fusion process and enhancing the use of multisource information. Additionally, FFDM-F achieves over a 10% improvement in precision for small fire detection compared to traditional methods and reduces misidentifications associated with single-source images. This research contributes to multisource image fusion for forest fire detection, providing a more accurate and reliable early warning tool and laying the foundation for future work in this field.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"6617-6629"},"PeriodicalIF":4.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10884036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553290","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":"Wilcoxon Nonparametric CFAR Scheme for Ship Detection in SAR Image","authors":"Xiangwei Meng","doi":"10.1109/JSTARS.2025.3533140","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3533140","url":null,"abstract":"The parametric constant false alarm rate (CFAR) detection algorithms, which are based on various statistical distributions, such as Gaussian, Gamma, Weibull, log-normal, <inline-formula><tex-math>$G^{0}$</tex-math></inline-formula>, and alpha-stable distribution, are most widely used to detect the ship targets in SAR images at present. However, the clutter background in SAR images is complicated and variable. When the actual clutter background deviates from the assumed statistical distribution, the performance of the parametric CFAR detector deteriorates, whereas the advantage of the nonparametric CFAR detector that its false alarm rate is independent of the background distribution is exhibited. In this work, the Wilcoxon nonparametric CFAR scheme for the ship detection in SAR images is proposed and analyzed, and a closed form of the false alarm rate for the Wilcoxon nonparametric CFAR detector to determine the decision threshold is presented. By comparison with several typical parametric CFAR schemes on Sentinel-1A, ICEYE-X6, and Gaofen-3 SAR images, the robustness of the ability of the Wilcoxon nonparametric CFAR detector to control the actual false alarm rate at a suitably low level in different detection backgrounds is revealed, and its detection performance for the weak ships in the rough sea backgrounds is evidently improved. Moreover, the detection speed of the Wilcoxon nonparametric CFAR detector is fast, and it has a simple hardware implementation.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"5360-5377"},"PeriodicalIF":4.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10886933","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489141","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 Novel Two-Stage Learning-Based Phase Unwrapping Algorithm via Multimodel Fusion","authors":"Chao Yan;Tao Li;Yandong Gao;Shijin Li;Xiang Zhang;Xuefei Zhang;Di Zhang;Huiqin Liu","doi":"10.1109/JSTARS.2025.3541322","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3541322","url":null,"abstract":"Phase unwrapping (PhU) is one of the key steps in interferometric synthetic aperture radar (InSAR) data processing, and it is a considerable challenge for PhU in regions with high-noise and large-gradient changes. Deep learning phase unwrapping (DLPU) can better solve this problem. However, a single DLPU algorithm still finds it difficult to obtain robust PhU results in regions with large-gradient changes. In addition, the performance of the same training model varies greatly for different data. To solve this problem, this paper combines a deep neural network model with the traditional PhU model and proposes a novel two-stage learning-based phase unwrapping (TLPU) algorithm via multimodel fusion. The major advantages of TLPU are as follows: 1) A high-resolution U-Net (HRU-Net) model trained on a dataset constructed according to InSAR interferometric geometry is utilized for the PhU for the first time, which effectively improves the performance of the DLPU. 2) TLPU utilizes the traditional PhU method to optimize the results of DLPU, addressing the issue of weak generalization ability of a single DLPU, while improving accuracy in areas with large-gradient changes. Experimental analysis was carried out using LT-1 data, and the results show that the proposed TLPU algorithm can achieve superior excellent results in large-gradient change regions compared with the commonly used PhU method, with root mean square errors of only 1.63 rad in experiment 1 and 1.96 rad in experiment 2.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"7468-7479"},"PeriodicalIF":4.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10884057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645154","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}