IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献_第6页

CUG_MISDataset: A Remote Sensing Instance Segmentation Dataset for Improved Wide-Area High-Precision Mining Land Occupation Recognition CUG_MISDataset：用于改进广域高精度采矿占地识别的遥感实例分割数据集

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-11 DOI: 10.1109/JSTARS.2024.3454333

Yuqian Zhu;Weitao Chen;Wenxi He;Ruizhen Wang;Xianju Li;Lizhe Wang

{"title":"CUG_MISDataset: A Remote Sensing Instance Segmentation Dataset for Improved Wide-Area High-Precision Mining Land Occupation Recognition","authors":"Yuqian Zhu;Weitao Chen;Wenxi He;Ruizhen Wang;Xianju Li;Lizhe Wang","doi":"10.1109/JSTARS.2024.3454333","DOIUrl":"10.1109/JSTARS.2024.3454333","url":null,"abstract":"The effective and rapid acquisition of wide-area mine occupation information is crucial for ecological geo-environmental protection and sustainable development. Remote sensing instance segmentation technology based on deep learning is a promising solution. However, there are two significant challenges including insufficient training datasets and unsuitable segmentation models. To overcome these issues, this study provides a large-scale remote sensing instance segmentation dataset for mining land occupation (CUG_MISDataset). The CUG_MISDataset comprises 1426 image blocks and more than 3000 instances, covering all 150 types of mines found in China's Hubei province. It features multiple mine types, various land occupations, and complex instance scales. First, this study compares the performance of seven mainstream remote sensing instance segmentation models using the proposed CUG_MISDataset. The results show that all seven models achieve high segmentation accuracy. It indicates that the constructed CUG_MISDataset is robust and can serve as a valuable benchmark for remote sensing instance segmentation of mining areas. Second, aiming at the difficulty of large scale variation in this dataset, we propose a multiscale dilation feature pyramid network (MSD-FPN), which introduces a dynamic weight allocation mechanism to give more weight to important semantic information, while convolution with different dilation rates is used in the module to enhance the expression of mines’ multiscale features. The proposed MSD-FPN can achieve a 2.0% average precision improvement on the CUG_MISDataset.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193206","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}

引用次数: 0

Microphysical Insights From Dual Polarization Spectral Observations in Updraft and Mixed-Phase Precipitation 从上升气流和混合相降水的双偏振光谱观测中获得微观物理见解

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-11 DOI: 10.1109/JSTARS.2024.3458890

Ivan Arias Hernandez;V. Chandrasekar

{"title":"Microphysical Insights From Dual Polarization Spectral Observations in Updraft and Mixed-Phase Precipitation","authors":"Ivan Arias Hernandez;V. Chandrasekar","doi":"10.1109/JSTARS.2024.3458890","DOIUrl":"10.1109/JSTARS.2024.3458890","url":null,"abstract":"This study presents dual polarization spectral analysis over updraft environments in thunderstorms. The composition and interaction of particle in updrafts environments are analyzed using the expansion that the spectral polarimetric provides in radar variables. Updrafts are identified using dual Doppler analysis and polarimetric signatures such as \u0000<inline-formula><tex-math>$Z_{text{dr}}$</tex-math></inline-formula>\u0000 columns. Spectral techniques are presented to estimate the spectrum in updraft and mixed-phase environments. A separation in the spectrum of melting ice and rain was observed in the analyzed updrafts. This separation in the spectrum allows us to study the polarimetric characteristics and interaction of melting ice and rain. Furthermore, scattering simulations were carried out to validate the spectral signatures attributed to melting ice. It was found that small ice particles mixed with rain can produced similar polarimetric characteristics as the one observed in the spectral analysis. Conceptual models based on the spectral observations are presented to illustrate the interaction of ice particles with a recirculation and a vigorous updraft.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10678847","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193203","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}

引用次数: 0

AFUNet With Active Contour Loss for Water Body Detection in SAR Imagery 利用主动轮廓损失的 AFUNet 在合成孔径雷达图像中进行水体探测

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-11 DOI: 10.1109/JSTARS.2024.3459624

Bin Han;Guangao Xing;Xiaozhen Lu;Anup Basu

{"title":"AFUNet With Active Contour Loss for Water Body Detection in SAR Imagery","authors":"Bin Han;Guangao Xing;Xiaozhen Lu;Anup Basu","doi":"10.1109/JSTARS.2024.3459624","DOIUrl":"10.1109/JSTARS.2024.3459624","url":null,"abstract":"With advancements in remote sensing technology, synthetic aperture radar (SAR) imagery has become one of the main methods to detect surface water bodies. The detection of water bodies in SAR imagery remains a challenging task due to the presence of complex interference. To achieve accurate water body detection, we proposed an attention fusion U-net inspired by the effectiveness of U-net in segmenting small targets with weak edges. First, the spatial attention module and channel attention module are added to the skip connections between encoder and decoder parts to extract useful low- and high-level features, thereby compensating for the loss of semantic information of downsampling. Second, the multiscale convolutional pooling block is introduced into the encoder part to better utilize the contextual information, capturing water and land features at different scales. Third, considering the feature distortion resulting from upsampling, an attentional upsampler (AU) is designed to facilitate lossless feature fusion. Furthermore, an active contour loss is designed as additional regularization to learn more boundary information, improving the model's segmentation performance. The water body detection experiments on the ALOS phased array L-band SAR and Sen1-SAR datasets demonstrate that the presented AFUNet outperforms the related start-of-the-art methods in detection accuracy in terms of five evaluation metrics.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10678920","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193200","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}

引用次数: 0

Optimizing Participant Selection for Fault-Tolerant Decision Making in Orbit Using Mixed Integer Linear Programming 利用混合整数线性规划优化轨道容错决策的参与者选择

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-11 DOI: 10.1109/JSTARS.2024.3459630

Robert Cowlishaw;Annalisa Riccardi;Ashwin Arulselvan

{"title":"Optimizing Participant Selection for Fault-Tolerant Decision Making in Orbit Using Mixed Integer Linear Programming","authors":"Robert Cowlishaw;Annalisa Riccardi;Ashwin Arulselvan","doi":"10.1109/JSTARS.2024.3459630","DOIUrl":"10.1109/JSTARS.2024.3459630","url":null,"abstract":"In challenging environments such as space, where decisions made by a network of satellites can be prone to inaccuracies or biases, leveraging smarter systems for onboard data processing, decision making is becoming increasingly common. To ensure fault tolerance within the network, consensus mechanisms play a crucial role. However, in a dynamically changing network topology, achieving consensus among all satellites can become excessively time consuming. To address this issue, the practical Byzantine fault-tolerance algorithm is employed, utilizing satellite trajectories as input to determine the time required for achieving consensus across a subnetwork of satellites. To optimize the selection of subsets for consensus, a mixed integer linear programming approach is developed. This method is then applied to analyze the characteristics of optimal subsets using satellites from the International Charter: Space and Major Disasters (ICSMD) over a predefined maximum time horizon. Results indicate that consensus within these satellites can be reached in less than 3.3 h in half of cases studied. Two satellites that are within the maximum communication range at all times are oversubscribed for taking part in the subnetwork. A further analysis has been completed to analyze which are the best set of orbital parameters for taking part in a consensus network as part of the ICSMD.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10678934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193196","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}

引用次数: 0

TrackingMamba: Visual State Space Model for Object Tracking TrackingMamba：用于物体跟踪的视觉状态空间模型

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-11 DOI: 10.1109/JSTARS.2024.3458938

Qingwang Wang;Liyao Zhou;Pengcheng Jin;Xin Qu;Hangwei Zhong;Haochen Song;Tao Shen

{"title":"TrackingMamba: Visual State Space Model for Object Tracking","authors":"Qingwang Wang;Liyao Zhou;Pengcheng Jin;Xin Qu;Hangwei Zhong;Haochen Song;Tao Shen","doi":"10.1109/JSTARS.2024.3458938","DOIUrl":"10.1109/JSTARS.2024.3458938","url":null,"abstract":"In recent years, UAV object tracking has provided technical support across various fields. Most existing work relies on convolutional neural networks (CNNs) or visual transformers. However, CNNs have limited receptive fields, resulting in suboptimal performance, while transformers require substantial computational resources, making training and inference challenging. Mountainous and jungle environments-critical components of the Earth's surface and key scenarios for UAV object tracking-present unique challenges due to steep terrain, dense vegetation, and rapidly changing weather conditions, which complicate UAV tracking. The lack of relevant datasets further reduces tracking accuracy. This article introduces a new tracking framework based on a state-space model called TrackingMamba, which uses a single-stream tracking architecture with Vision Mamba as its backbone. TrackingMamba not only matches transformer-based trackers in global feature extraction and long-range dependence modeling but also maintains computational efficiency with linear growth. Compared to other advanced trackers, TrackingMamba delivers higher accuracy with a simpler model framework, fewer parameters, and reduced FLOPs. Specifically, on the UAV123 benchmark, TrackingMamba outperforms the baseline model OSTtrack-256, improving AUC by 2.59% and Precision by 4.42%, while reducing parameters by 95.52% and FLOPs by 95.02%. The article also evaluates the performance and shortcomings of TrackingMamba and other advanced trackers in the complex and critical context of jungle environments, and it explores potential future research directions in UAV jungle object tracking.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10678881","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193215","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}

引用次数: 0

A Generalized Deep Learning-Based Method for Rapid Co-Seismic Landslide Mapping 基于广义深度学习的同震滑坡快速测绘方法

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-11 DOI: 10.1109/JSTARS.2024.3457766

Jing Yang;Mingtao Ding;Wubiao Huang;Zhenhong Li;Zhengyang Zhang;Jing Wu;Jianbing Peng

{"title":"A Generalized Deep Learning-Based Method for Rapid Co-Seismic Landslide Mapping","authors":"Jing Yang;Mingtao Ding;Wubiao Huang;Zhenhong Li;Zhengyang Zhang;Jing Wu;Jianbing Peng","doi":"10.1109/JSTARS.2024.3457766","DOIUrl":"10.1109/JSTARS.2024.3457766","url":null,"abstract":"The rapid mapping of co-seismic landslides is essential for emergency management and loss assessment. Deep learning algorithms generally follow a supervised learning workflow, where the trained model is used to predict landslides in surrounding areas, achieving landslide mapping with high accuracy. For a new study area landslide extraction task, the performance of the model trained on a specific dataset will be greatly reduced due to the varying data distribution of co-seismic landslides. Considering the urgent need for large-scale co-seismic landslide mapping, we developed a generalized deep learning-based landslide identification method. First, a new model—ResU-SENet is developed to generate semantic segmentation maps of landslides. The proposed model adaptively emphasizes the channel-wise weights of the input data. Three multidomain models are then designed by combining annotated landslide samples from two different domains to improve the model generalization ability. Finally, the trained models are applied directly to completely unknown domains to test model generalizability. Experiments in Iburi and Jiuzhaigou showed that the proposed model yielded the recall values of 5.93% and 7.51% higher than ResU-Net. The adoption of multidomain models effectively reduced the number of new training samples required by 50% and maintained a similar identification performance as if trained entirely with new samples. Applying the models trained by Jiuzhaigou and Iburi samples directly to Palu, the F1-score under the ResU-SENet model reached 0.6875. Moreover, the connections between model generalization and data distribution was demonstrated. This work could provide a fast response for future large-scale co-seismic landslide mapping.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193199","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}

引用次数: 0

Reperceive Global Vision of Transformer for Remote Sensing Images Weakly Supervised Object Localization 重新感知遥感图像弱监督目标定位的变压器全局视图

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-11 DOI: 10.1109/JSTARS.2024.3459792

Xuran Hu;Mingzhe Zhu;Zhenpeng Feng;Ljubiša Stanković

{"title":"Reperceive Global Vision of Transformer for Remote Sensing Images Weakly Supervised Object Localization","authors":"Xuran Hu;Mingzhe Zhu;Zhenpeng Feng;Ljubiša Stanković","doi":"10.1109/JSTARS.2024.3459792","DOIUrl":"10.1109/JSTARS.2024.3459792","url":null,"abstract":"In recent decades, weakly supervised object localization (WSOL) has gained increasing attention in remote sensing. However, unlike optical images, remote sensing images (RSIs) often contain more complex scenes, which poses challenges for WSOL. Traditional convolutional neural network (CNN)-based WSOL methods are often limited by a small receptive field and yield unsatisfactory results. Transformer-based methods can obtain global perception, addressing the limitations of receptive fields in CNN-based methods, yet it may also introduce attention diffusion. To address the aforementioned problems, this article proposes a novel WSOL method based on an interpretable vision transformer (ViT), RPGV. We introduce a feature fusion enhancement module to obtain the saliency map that captures global information. Simultaneously, we solve the problem of discrete attention in the traditional ViT and eliminate local distortion in the feature map by introducing a global semantic screening module. We conduct comprehensive experiments on DIOR and HRRSD datasets, demonstrating the superior performance of our method compared to current state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10678922","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193198","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}

引用次数: 0

Physics-based Machine Learning Emulator of At-sensor Radiances for Solar-induced Fluorescence Retrieval in the O$_{2}$-A Absorption Band 基于物理学的传感器辐射模拟器，用于 O$_{2}$-A 吸收波段的太阳诱导荧光检索

IF 5.5 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-10 DOI: 10.1109/jstars.2024.3457231

Miguel Pato, Jim Buffat, Kevin Alonso, Stefan Auer, Emiliano Carmona, Stefan Maier, Rupert Müller, Patrick Rademske, Uwe Rascher, Hanno Scharr

引用次数: 0

Hyperspectral Band Selection via Joint Volume Gradient 通过联合体积梯度选择高光谱波段

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-10 DOI: 10.1109/JSTARS.2024.3457671

Songyi Xiao;Liangliang Zhu;Shouzhi Li;Luyan Ji;Xiurui Geng

{"title":"Hyperspectral Band Selection via Joint Volume Gradient","authors":"Songyi Xiao;Liangliang Zhu;Shouzhi Li;Luyan Ji;Xiurui Geng","doi":"10.1109/JSTARS.2024.3457671","DOIUrl":"10.1109/JSTARS.2024.3457671","url":null,"abstract":"Unsupervised band selection (BS) is a crucial research direction in the domain of hyperspectral image (HSI) processing. In recent years, volume-based criteria have garnered considerable attention, with the volume-gradient-based BS (VGBS) algorithm being particularly notable. However, we have identified that VGBS inherently suffers from the local extremum problem due to its reliance on the original volume gradient formula, which only permits the removal of a single band per iteration. To address this issue, we introduce the concept of joint volume gradient (JVG) through a novel determinant formula for the high-order mixed product expansion. We then propose an enhanced version of VGBS, termed JVG-based BS (JVGBS), which allows for the simultaneous deletion of multiple bands. Moreover, a simplified objective function of JVG is developed to mitigate the high computational complexity associated with calculating volume metrics when a small number of bands is removed at once. Regarding the complexity imposed by the large cardinality of traversing matrix column combinations, we provide an exemplary algorithm employing groupwise strategies to achieve rapid computational acceleration. Experimental results on Gaofen-5 and publicly available hyperspectral datasets demonstrate that the proposed algorithms have rather superior performance against state-of-the-art competitors in terms of both computational complexity and classification accuracy.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193208","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}

引用次数: 0

Road Structure Inspired UGV-Satellite Cross-View Geo-Localization 受道路结构启发的 UGV 卫星跨视角地理定位

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-10 DOI: 10.1109/JSTARS.2024.3457756

Di Hu;Xia Yuan;Huiying Xi;Jie Li;Zhenbo Song;Fengchao Xiong;Kai Zhang;Chunxia Zhao

{"title":"Road Structure Inspired UGV-Satellite Cross-View Geo-Localization","authors":"Di Hu;Xia Yuan;Huiying Xi;Jie Li;Zhenbo Song;Fengchao Xiong;Kai Zhang;Chunxia Zhao","doi":"10.1109/JSTARS.2024.3457756","DOIUrl":"10.1109/JSTARS.2024.3457756","url":null,"abstract":"This article presents a new approach to address the challenge of combining ground-based LiDAR data with satellite images for cross-view image geo-localization. The task is to figure out the position and orientation of the LiDAR within the given satellite image. While previous research has mainly focused on imagery, the integration of ground-based point clouds with satellite images has been limited due to significant differences in modalities. To release this limitation, we propose a novel method that utilizes the road structure as a consistent reference between satellite images and ground LiDAR data for accurate geo-localization. Our methodology encompasses the extraction of road structures from both point clouds and satellite images. To extract road structures from point clouds, we leverage the enhanced viewpoint beam model, which effectively captures the spatial characteristics of ground landmarks. In addition, we utilize fractional-order differential-based super-resolution technology for satellite images to improve road structure detection, ensuring reliable performance across different altitudes. Following this, our approach involves matching road structures from the ground and satellite views, simplifying the localization process to a template-matching task. Consequently, we successfully address the challenge of accurately determining the 3-DoF pose of the LiDAR within the satellite image context. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in geo-localization, outperforming comparable methods. In addition, the approach shows versatility across various altitudes.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193201","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}

引用次数: 0