IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第10页

Dual-Polarization Responses of Microwave Radiation of Diorite in Process of Uniaxial Loading 单轴加载过程中闪长岩的微波辐射双极化响应

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2024-11-06 DOI: 10.1109/LGRS.2024.3492325

Guangrui Dong;Wenfei Mao;Licheng Sun;Tao Zheng;Haofeng Dou;Lixin Wu

{"title":"Dual-Polarization Responses of Microwave Radiation of Diorite in Process of Uniaxial Loading","authors":"Guangrui Dong;Wenfei Mao;Licheng Sun;Tao Zheng;Haofeng Dou;Lixin Wu","doi":"10.1109/LGRS.2024.3492325","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3492325","url":null,"abstract":"The experimental detection of the changes in microwave radiation of rocks under pressure is key to identifying earthquake anomalies through satellite passive microwave remote sensing. However, such changes have not been comprehensively characterized due to the considerable differences in crustal lithology, weak microwave radiation signals, and strong environmental noise. Considering the intrinsic and significant diversity of different polarized microwave radiations of any materials, this study investigated the responses of different polarized microwave radiations during loading the rock materials. Thus, a synchronized detection system including multiple sensors was constructed at outdoor to reveal the stress-induced changes in C-band microwave brightness temperature (MBT) of diorite specimen. Experimental results show that both the horizontal and vertical MBT varied regularly with the changes of pressure; however, the trends of changes of MBT were greatly influenced by the polarization modes. Specifically, a positive correlation was illustrated between the change in vertical polarization MBT and cyclically varied pressure, during which the MBT changed with a rate of 0.033 K/MPa about. In contrast, the changes in horizontal polarization MBT exhibited a negative correlation with the varied pressure, and the MBT change rate was approximately −0.031 K/MPa. Based on the radiative transfer theory, it was found that the opposite MBT changes with respect to h- and v-polarizations are supposed to be caused by the dielectric anisotropy under uniaxial compression conditions. This study illustrates the significant and discernible MBT changes of diorite induced by the stress, which is helpful to identify the detectable microwave radiation anomalies before large earthquake occurrence.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645518","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}

引用次数: 0

Domain-Invariant Progressive Knowledge Distillation for UAV-Based Object Detection 基于无人机的物体检测的领域不变渐进式知识提炼

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2024-11-06 DOI: 10.1109/LGRS.2024.3492187

Liang Yao;Fan Liu;Chuanyi Zhang;Zhiquan Ou;Ting Wu

{"title":"Domain-Invariant Progressive Knowledge Distillation for UAV-Based Object Detection","authors":"Liang Yao;Fan Liu;Chuanyi Zhang;Zhiquan Ou;Ting Wu","doi":"10.1109/LGRS.2024.3492187","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3492187","url":null,"abstract":"Knowledge distillation (KD) is an effective method for compressing models in object detection tasks. Due to limited computational capability, unmanned aerial vehicle-based object detection (UAV-OD) widely adopt the KD technique to obtain lightweight detectors. Existing methods often overlook the significant differences in feature space caused by the large gap in scale between the teacher and student models. This limitation hampers the efficiency of knowledge transfer during the distillation process. Furthermore, the complex backgrounds in aerial images make it challenging for the student model to efficiently learn the object features. In this letter, we propose a novel KD framework for UAV-OD. Specifically, a progressive distillation approach is designed to alleviate the feature gap between teacher and student models. Then, a new feature alignment method is provided to extract object-related features for enhancing the student model’s knowledge reception efficiency. Finally, extensive experiments are conducted to validate the effectiveness of our proposed approach. The results demonstrate that our proposed method achieves state-of-the-art performance on two datasets.","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":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691665","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}

引用次数: 0

MD-NeRF: Enhancing Large-Scale Scene Rendering and Synthesis With Hybrid Point Sampling and Adaptive Scene Decomposition MD-NeRF：利用混合点采样和自适应场景分解增强大规模场景渲染和合成功能

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2024-11-06 DOI: 10.1109/LGRS.2024.3492208

Yichen Zhang;Zhi Gao;Wenbo Sun;Yao Lu;Yuhan Zhu

{"title":"MD-NeRF: Enhancing Large-Scale Scene Rendering and Synthesis With Hybrid Point Sampling and Adaptive Scene Decomposition","authors":"Yichen Zhang;Zhi Gao;Wenbo Sun;Yao Lu;Yuhan Zhu","doi":"10.1109/LGRS.2024.3492208","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3492208","url":null,"abstract":"Neural radiance fields (NeRFs) have gained great success in 3-D representation and novel-view synthesis, which attracted great efforts devoted to this area. However, when rendering large-scale scenes from a drone perspective, existing NeRF methods exhibit pronounced distortions in scene detail including absent textures and blurring of small objects. In this letter, we propose MD-NeRF to mitigate such distortions by integrating a hybrid sampling strategy and an adaptive scene decomposition method. Specifically, an anti-aliasing sampling method combining spiral sampling and sampling along rays is presented to address rendering anomalies. In addition, we decompose a large scene into multiple subscenes using a mixture of expert (MoE) modules. A shared expert is introduced to capture common features and reduce redundancy across the specialized experts. Consequently, the combination of these two methods effectively minimizes distortions when rendering large-scale scenes and enables our model to produce finer textures and more coherent details. We have conducted extensive experiments on several large-scale unbounded scene datasets, and the results demonstrate that our approach has achieved state-of-the-art performance on all datasets, most notably evidenced by a 1-dB enhancement in PSNR metrics on the Mill19 dataset.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636511","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}

引用次数: 0

LiDAR-Guided Stereo Matching Using Bayesian Optimization With Gaussian Process Regression 利用贝叶斯优化和高斯过程回归进行激光雷达引导的立体匹配

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2024-11-06 DOI: 10.1109/LGRS.2024.3492175

Hao Yi;Bo Liu;Bin Zhao;Enhai Liu

{"title":"LiDAR-Guided Stereo Matching Using Bayesian Optimization With Gaussian Process Regression","authors":"Hao Yi;Bo Liu;Bin Zhao;Enhai Liu","doi":"10.1109/LGRS.2024.3492175","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3492175","url":null,"abstract":"LiDAR-guided stereo matching for high-precision disparity estimation is a very promising task in photogrammetry and remote sensing. Unfortunately, existing methods suffer from the problem that it is difficult to automatically obtain appropriate stereo matching model parameters to ensure satisfactory results. To solve it, this letter proposes a LiDAR-guided stereo matching framework using Bayesian optimization with Gaussian process regression, which aims to automatically infer the stereo matching model parameters by LiDAR data. First, local matching model based on the belief propagation algorithm is designed. Second, the objective function is constructed by minimizing the difference between the local matching results and the LiDAR data. Third, Bayesian optimization with Gaussian process regression is applied to minimize this objective function to infer the model parameters. Finally, experimental results on the GaoFen-7 and UAV Stereo datasets show that the proposed method can effectively infer suitable model parameters from LiDAR data, and our method outperforms the state-of-the-art methods.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636271","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}

引用次数: 0

Bistatic SAR Automatic Target Recognition With Multichannel Multiview Feature Fusion Network 利用多通道多视角特征融合网络进行双向合成孔径雷达自动目标识别

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2024-11-05 DOI: 10.1109/LGRS.2024.3491842

Zhe Geng;Wei Li;Xiang Yu;Daiyin Zhu

{"title":"Bistatic SAR Automatic Target Recognition With Multichannel Multiview Feature Fusion Network","authors":"Zhe Geng;Wei Li;Xiang Yu;Daiyin Zhu","doi":"10.1109/LGRS.2024.3491842","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3491842","url":null,"abstract":"Bistatic synthetic aperture radar (SAR) with spatially separated transmitter (TX) and receiver (RX) is advantageous over monostatic SAR systems in trajectory flexibility and antistealth/antijamming capability. On the other hand, since bistatic SAR imaging involves more technical complexities and incurs higher cost, the research in the field of bistatic automatic target recognition (ATR) has been mainly relying on simulated SAR imagery. Reckoning with the lack of supporting database in the public domain, the researchers at Nanjing University of Aeronautics and Astronautics (NUAA) constructed a proprietary bistatic SAR database featuring multiple types of representative military vehicles with the self-developed miniSAR system. Moreover, a multichannel multiview feature fusion network (MMFFN) is devised by incorporating the vision transformer (ViT). The simulation results show that the proposed MMFFN offers a classification accuracy improvement of 4.86%–16.63% over the baseline network (i.e., the plain ViT) in a series of experiments featuring small-to-large observation angle deviations between the training and test data.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645520","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}

引用次数: 0

Dual Branch Masked Transformer for Hyperspectral Image Classification 用于高光谱图像分类的双分支屏蔽变换器

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2024-11-05 DOI: 10.1109/LGRS.2024.3490534

Kuo Li;Yushi Chen;Lingbo Huang

{"title":"Dual Branch Masked Transformer for Hyperspectral Image Classification","authors":"Kuo Li;Yushi Chen;Lingbo Huang","doi":"10.1109/LGRS.2024.3490534","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3490534","url":null,"abstract":"Transformer has been widely used in hyperspectral image (HSI) classification tasks because of its ability to capture long-range dependencies. However, most Transformer-based classification methods lack the extraction of local information or do not combine spatial and spectral information well, resulting in insufficient extraction of features. To address these issues, in this study, a dual-branch masked Transformer (Dual-MTr) model is proposed. Masked Transformer (MTr) is used to pretrain vision transformer (ViT) by reconstruction of both masked spatial image and spectral spectrum, which embeds the local bias by the process of recovering from localized patches to the global original input. Different tokenization methods are used for different types of input data. Patch embedding with overlapping regions is used for 2-D spatial data and group embedding is used for 1-D spectral data. Supervised learning has been added to the pretraining process to enhance strong discriminability. Then, the dual-branch structure is proposed to combine the spatial and spectral features. To strengthen the connection between the two branches better, Kullback-Leibler (KL) divergence is used to measure the differences between the classification results of the two branches, and the loss resulting from the computed differences is incorporated into the training process. Experimental results from two hyperspectral datasets demonstrate the effectiveness of the proposed method compared to other methods.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636351","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}

引用次数: 0

Learning Gradient Descent to Optimize DAS Signal Estimation 学习梯度下降法优化 DAS 信号估计

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2024-11-04 DOI: 10.1109/LGRS.2024.3490732

Haitao Ma;Mengyang Yuan;Ning Wu;Yue Li;Yanan Tian

{"title":"Learning Gradient Descent to Optimize DAS Signal Estimation","authors":"Haitao Ma;Mengyang Yuan;Ning Wu;Yue Li;Yanan Tian","doi":"10.1109/LGRS.2024.3490732","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3490732","url":null,"abstract":"For subsequent seismic data processing and interpretation, it is important to obtain high-quality distributed acoustic sensing (DAS) signals from down-hole DAS data containing various complex noises. Model-based denoising methods mainly treat this signal estimation issue as a maximum a posteriori (MAP) optimization problem, for its relatively transparent mathematical model and wide range of applications. However, the manually designed prior assumption in MAP cannot accurately describe the actual distribution of DAS data, so the optimization parameters for obtaining high-quality solutions are difficult to determine, making it unavailable in DAS signal estimation. To solve these problems, we propose to emulate the optimization process of MAP with neural networks and accomplish the signal estimation task in feature space via some customized optimization modules. Specifically, we first construct an optimization unit (OPTU) to simulate the optimization process. And then, in order to further obtain the signal distribution of DAS data, we design in each OPTU, a multiscale dense feature aggregation (MDFA) module with the idea of back-projection fusion. With the help of OPTU, the optimization estimation process would be implemented more finely and automatically, expanding the application of MAP for accurate DAS signal estimation. Experiments on both synthetic and field DAS data demonstrate that our method can successfully estimate the high-quality signals from DAS data corrupted by complex noises, with less energy loss.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636488","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}

引用次数: 0

Remote Sensing Framework for Evaluating Forest Landscape Restoration Projects: Enhancing Accuracy and Effectiveness 评估森林景观恢复项目的遥感框架：提高准确性和有效性

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2024-11-04 DOI: 10.1109/LGRS.2024.3491372

Michelle C. A. Picoli;Kenny Helsen

{"title":"Remote Sensing Framework for Evaluating Forest Landscape Restoration Projects: Enhancing Accuracy and Effectiveness","authors":"Michelle C. A. Picoli;Kenny Helsen","doi":"10.1109/LGRS.2024.3491372","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3491372","url":null,"abstract":"Forest and landscape restoration (FLR) initiatives are essential for combating deforestation, preserving biodiversity, and mitigating climate change. Remote sensing emerges as a key tool in evaluating FLR projects by providing accurate and timely data for monitoring and assessment. This letter presents a framework for generating high-quality maps using remote sensing data to assess the biophysical impact of FLR projects. The framework was applied to evaluate the Katanino FLR Project in Zambia. The results showcase a remarkable increase in forest cover, with a forest classification accuracy exceeding 90%. Such encouraging outcomes underscore the efficacy of the project in achieving its restoration goals and highlight the tangible benefits of employing remote sensing tools in FLR evaluation. Moreover, comprehensive FLR assessment, when complemented with diverse evaluation methodologies, facilitates a holistic understanding of FLR project impacts, enabling informed decision-making for the sustainable management of forest landscapes worldwide.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636250","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}

引用次数: 0

A Cloud Motion Estimation Method Based on Cloud Image Depth Feature Matching 基于云图像深度特征匹配的云运动估计方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2024-11-04 DOI: 10.1109/LGRS.2024.3491094

Lianglin Zou;Ping Tang;Yisen Niu;Zixuan Yan;Xilong Lin;Jifeng Song;Qian Wang

{"title":"A Cloud Motion Estimation Method Based on Cloud Image Depth Feature Matching","authors":"Lianglin Zou;Ping Tang;Yisen Niu;Zixuan Yan;Xilong Lin;Jifeng Song;Qian Wang","doi":"10.1109/LGRS.2024.3491094","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3491094","url":null,"abstract":"The movement of clouds directly influences fluctuations in solar radiation. Therefore, cloud motion vector (CMV) estimation techniques are widely applied in sequential cloud images to predict solar radiation and study other meteorologically related fields. However, traditional block matching, optical flow, and feature point methods struggle to accurately capture the deformation, multilayered, and mixed cloud types’ motion due to the lack of deep semantic understanding of cloud images. Additionally, without cloud-motion-labeled, deep learning tools such as CNNs are limited in their utility for motion assessment. Therefore, this letter proposes a method of cloud image depth feature matching to assess the CMV in time series, including image enhancement, self-supervised feature extraction, feature matching, feature fusion, and spatiotemporal filtering. Experimental results demonstrate a significant improvement in accuracy compared to traditional CMV estimation techniques, with higher robustness observed across various complex cloud scenarios.","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":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691739","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}

引用次数: 0

A Novel PU Method for Mining Area Based on Edge Detection Using the SegNet Model 使用 SegNet 模型基于边缘检测挖掘区域的新型 PU 方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2024-11-04 DOI: 10.1109/LGRS.2024.3490552

Baojing Zhang;Zhiyong Wang;Zhenjin Li;Wenfu Yang;Weibing Li

{"title":"A Novel PU Method for Mining Area Based on Edge Detection Using the SegNet Model","authors":"Baojing Zhang;Zhiyong Wang;Zhenjin Li;Wenfu Yang;Weibing Li","doi":"10.1109/LGRS.2024.3490552","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3490552","url":null,"abstract":"Due to the large deformation gradient caused by mining, it is easy to cause serious incoherence phenomenon in radar interferometry, and the traditional phase unwrapping (PU) method is limited in this case. To solve this problem, a novel PU method for mining area based on edge detection using the SegNet model is proposed for mining subsidence basins with large deformation. First, SegNet network was used to extract the edge information of the subsidence basin in the mining area. Then, the edges were refined and connected by the Zhang-Suen thinning method and regional growth method, respectively. Finally, PU was completed by the determined phase jump variables. Simulated interferograms with different signal-to-noise ratio (SNR) and two real interferograms with different interference qualities are selected for experiments. Compared with the three traditional PU methods and two deep learning PU methods, the proposed model has higher accuracy and better robustness. When the SNR is 1 and 4, the unwrapping error distribution area of the proposed method is the smallest, and the PU result is more close to the real situation in the interferogram of real mining area. The novel two-step PU method effectively solves the problem that the traditional PU method is seriously affected by noise and large deformation.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"21 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645543","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}

引用次数: 0