IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献_第4页

Probability Prediction Network With Checkerboard Prior for Lossless Remote Sensing Image Compression 用于无损遥感图像压缩的棋盘格先验概率预测网络

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-17 DOI: 10.1109/JSTARS.2024.3462948

Xuxiang Feng;Enjia Gu;Yongshan Zhang;An Li

{"title":"Probability Prediction Network With Checkerboard Prior for Lossless Remote Sensing Image Compression","authors":"Xuxiang Feng;Enjia Gu;Yongshan Zhang;An Li","doi":"10.1109/JSTARS.2024.3462948","DOIUrl":"10.1109/JSTARS.2024.3462948","url":null,"abstract":"Lossless remote sensing image compression aims to reduce the storage size of images without any information loss, ensuring that the decompressed image is identical to the original one. Most existing methods focus on lossy image compression that reduce the storage cost with certain data loss. It is challenging to perform lossless compression due to the very high-resolution images, long encoding–decoding time, and low compression efficiency. In this article, we propose a lossless compression framework that compresses remote sensing images in a coarse-to-fine manner. Specifically, checkerboard segmentation is applied on each image to generate six subimages from the main diagonal and counter-diagonal of each channel to maximally preserve the detail and structural information. The subimages from the main diagonal are initially compressed by a traditional compression method, while the subimages from the counter-diagonal are compressed channel by channel using our proposed probability prediction network (P2Net) and arithmetic coding with the previously encoded subimages from both the main diagonal and counter-diagonal as prior knowledge. The proposed P2Net consists of a upsampling module, a feature enhancement module, a downsampling module, and a probability prediction module to learn the discrete probability distribution of pixels. Lossless compression is conducted with arithmetic coding on the discrete probability distribution. To the best of our knowledge, this is the first deep learning-based lossless compression framework for three-channel remote sensing images. Experiments demonstrate that our framework outperforms the state-of-the-art methods and requires about 3.4 s to compress a 1024 \u0000<inline-formula><tex-math>$times , text{1024},times$</tex-math></inline-formula>\u0000 3 image with 2.9% efficiency improvement compared to JPEG XL.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10682549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248840","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

Arbitrary Oriented Few-Shot Object Detection in Remote Sensing Images 遥感图像中任意方向的少拍物体检测

IF 5.5 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-16 DOI: 10.1109/jstars.2024.3461165

Wei Wu, Chengeng Jiang, Liao Yang, Weisheng Wang, Quanjun Chen, Junjian Zhang, Haiping Yang, Zuohui Chen

引用次数: 0

Spatiotemporal Nonstationary Robust Modeling Between Luojia1-01 Night-Time Light Imagery and Urban Community Average Residence Price 珞珈 1-01 夜光成像与城市社区平均居住价格之间的时空非稳态鲁棒建模

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-16 DOI: 10.1109/JSTARS.2024.3456376

Chang Li;Linqing Zou;Yinfei He;Bo Huang;Yan Zhao

{"title":"Spatiotemporal Nonstationary Robust Modeling Between Luojia1-01 Night-Time Light Imagery and Urban Community Average Residence Price","authors":"Chang Li;Linqing Zou;Yinfei He;Bo Huang;Yan Zhao","doi":"10.1109/JSTARS.2024.3456376","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3456376","url":null,"abstract":"This article is the first to propose a novel spatiotemporal nonstationary robust modeling between high spatial resolution Luojia1-01 night-time light intensity (NTLI) and urban community average residence price (UCARP), which encodes the spatiotemporal independent variable NTLI based on a new proposed geographical coding (GeoCode) to enhance the explanatory power of NTLI and leverages geographically and temporally weighted regression (GTWR) based on a new proposed spatiotemporal anomaly detection (STAD) to remove spatiotemporal outliers and then to robustly estimate modeling result. UCARP data and Luojia1-01 NTL imagery obtained from Wuhan, China, in June, September and October 2018 were crawled and downloaded for the experiment, whose results show that GTWR performs better than geographically weighted regression and temporally weighted regression. The comparisons of GTWR with 1) original data; 2) GeoCode (GC); 3) STAD; 4) first STAD last GeoCode (STAD_GC), and 5) first GeoCode last STAD (GC_STAD) show that 1) the \u0000<italic>q\u0000 values of geographical detector corresponding to the above methods are 0.055, 0.407, 0.126, 0.666, and 0.671, respectively, during September; 2) the adjusted \u0000<italic>R\u00002\u0000 values of GTWR are 0.460, 0.488, 0.683, 0.693, and 0.697, respectively; and 3) the proposed spatiotemporal data processing scheme, i.e., GC_STAD, has the most robust and best precision. This article not only proposes a new spatiotemporal nonstationary robust modeling method between small-scale NTL and UCARP but also reveals its underlying mechanism.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329322","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

Quantification of GEDI Geolocation Error and its Influence on Elevation and Canopy Height 量化 GEDI 地理定位误差及其对海拔高度和树冠高度的影响

IF 5.5 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-16 DOI: 10.1109/jstars.2024.3461843

Cancan Yang, Daoli Peng, Kai Deng, Ling Jiang, Mingwei Zhao, Weisheng Zeng, Yakui Shao, Ni Wang

引用次数: 0

ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery ALSS-YOLO：用于无人机图像中近红外野生动物检测的自适应轻量级信道分割和洗牌网络

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-16 DOI: 10.1109/JSTARS.2024.3461172

Ang He;Xiaobo Li;Ximei Wu;Chengyue Su;Jing Chen;Sheng Xu;Xiaobin Guo

{"title":"ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery","authors":"Ang He;Xiaobo Li;Ximei Wu;Chengyue Su;Jing Chen;Sheng Xu;Xiaobin Guo","doi":"10.1109/JSTARS.2024.3461172","DOIUrl":"10.1109/JSTARS.2024.3461172","url":null,"abstract":"Unmanned aerial vehicles (UAVs) equipped with thermal infrared (TIR) cameras play a crucial role in combating nocturnal wildlife poaching. However, TIR images often face challenges such as jitter and wildlife overlap, necessitating UAVs to possess the capability to identify blurred and overlapping small targets. Current traditional lightweight networks deployed on UAVs struggle to extract features from blurry small targets. To address this issue, we developed ALSS-YOLO, an efficient and lightweight detector optimized for TIR aerial images. First, we propose a novel adaptive lightweight channel split and shuffling (ALSS) module. This module employs an adaptive channel split strategy to optimize feature extraction and integrates a channel shuffling mechanism to enhance information exchange between channels. This improves the extraction of blurry features, crucial for handling jitter-induced blur and overlapping targets. Second, we developed a lightweight coordinate attention (LCA) module that employs adaptive pooling and grouped convolution to integrate feature information across dimensions. This module ensures lightweight operation while maintaining high detection precision and robustness against jitter and target overlap. Additionally, we developed a single-channel focus module to aggregate the width and height information of each channel into 4-D channel fusion, which improves the feature representation efficiency of infrared images. Finally, we modify the localization loss function to emphasize the loss value associated with small objects to improve localization accuracy. Extensive experiments on the BIRDSAI and ISOD TIR UAV wildlife datasets show that ALSS-YOLO achieves state-of-the-art performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248843","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

DCEA: DETR With Concentrated Deformable Attention for End-to-End Ship Detection in SAR Images DCEA：在合成孔径雷达图像中使用具有集中可变形注意力的 DETR 进行端对端船舶探测

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-16 DOI: 10.1109/JSTARS.2024.3461723

Hai Lin;Jin Liu;Xingye Li;Lai Wei;Yuxin Liu;Bing Han;Zhongdai Wu

{"title":"DCEA: DETR With Concentrated Deformable Attention for End-to-End Ship Detection in SAR Images","authors":"Hai Lin;Jin Liu;Xingye Li;Lai Wei;Yuxin Liu;Bing Han;Zhongdai Wu","doi":"10.1109/JSTARS.2024.3461723","DOIUrl":"10.1109/JSTARS.2024.3461723","url":null,"abstract":"Recently, significant advancements have been achieved in optimizing algorithms for synthetic aperture radar (SAR) ship detection. Nevertheless, two challenges still impede further research as follows. 1) Mainstream methods, whether anchor-free or anchor-based, adhere to a dense paradigm, leading to substantial redundancy and limited adaptability. 2) Ship targets in SAR images exhibit large shape variations and scale differences, making it difficult to efficiently extract key features from background clutter. To tackle the aforementioned problems, we propose DETR with Concentrated dEformable Attention (DCEA), a query-based method for end-to-end optimization of the current pipeline. First, for the irregular shapes and sparse distribution of ships, the concentrated deformable attention is introduced to model the spatial positions of targets, simulating their geometric transformations with precision. Second, an attentionwise propagation module is designed to integrate local fine-grained information with global semantic information, improving the detection performance for objects across diverse scales. Finally, due to the lack of information exchange between object queries, a dimensionwise information mixing module is employed to incorporate key information from various dimensions to enhance their representation capability. To validate the superior performance of DCEA, we conduct extensive experiments on multiple public datasets, achieving mean average precision scores of 0.991, 0.929, and 0.962 on the SSDD, HRSID, and SAR-Ship-Dataset, respectively, with a model size of only 14.34M parameters and 44.4 giga floating point operations.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681295","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248846","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 Deep Semantic Segmentation Approach to Map Forest Tree Dieback in Sentinel-2 Data 绘制哨兵-2 数据中林木枯死图的深度语义分割方法

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-16 DOI: 10.1109/JSTARS.2024.3460981

Giuseppina Andresini;Annalisa Appice;Donato Malerba

引用次数: 0

Remote Sensing-Based Analysis of Urban Heat Island Driving Factors: A Local Climate Zone Perspective 基于遥感的城市热岛驱动因素分析：地方气候区视角

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-16 DOI: 10.1109/JSTARS.2024.3462537

Zhi Qiao;Ruoyu Jia;Jiawen Liu;Huan Gao;Qikun Wei

{"title":"Remote Sensing-Based Analysis of Urban Heat Island Driving Factors: A Local Climate Zone Perspective","authors":"Zhi Qiao;Ruoyu Jia;Jiawen Liu;Huan Gao;Qikun Wei","doi":"10.1109/JSTARS.2024.3462537","DOIUrl":"10.1109/JSTARS.2024.3462537","url":null,"abstract":"This study utilized multisource remote sensing data and advanced technology to investigate the potential driving factors of urban heat island (UHI) effects from the perspective of local climate zones (LCZs), including natural, social, and urban three-dimensional (3-D) structural factors. Using MODIS land surface temperature remote sensing data products and supplementary datasets, the simplified urban-extent algorithm was employed to identify UHI areas and quantify UHI Intensity (UHII). The stepwise multiple linear regression method and SHapley Additive exPlanations-explained eXtreme gradient boosting machine learning method were then applied to attribute UHII to 15 selected driving factors across 17 LCZ types in 369 Chinese cities. The findings indicate that large UHI areas are predominantly associated with low-rise LCZ types, where compact building arrangements intensify UHII, and increased building heights exacerbate this effect. During daytime, the UHI effects are largely driven by urban 3-D structures, particularly within LCZ 1-6 areas. Conversely, at night, the UHI effect is more significantly impacted by natural environmental factors. These insights offer a robust scientific foundation for urban planners to craft LCZ-specific strategies aimed at fostering the development of sustainable cities and communities.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248842","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

Ocean–Land Interface Determination From Mixed Waveform of Airborne Oceanic LiDAR 根据机载海洋激光雷达的混合波形确定海陆界面

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-16 DOI: 10.1109/JSTARS.2024.3462428

Jianfei Gao;Xinglei Zhao;Fengnian Zhou

{"title":"Ocean–Land Interface Determination From Mixed Waveform of Airborne Oceanic LiDAR","authors":"Jianfei Gao;Xinglei Zhao;Fengnian Zhou","doi":"10.1109/JSTARS.2024.3462428","DOIUrl":"10.1109/JSTARS.2024.3462428","url":null,"abstract":"Laser waveforms in airborne oceanic LiDAR (AOL) data are classified as either ocean or land waveforms. However, ocean and land may simultaneously exist along the ocean–land interface (OLI) in the large footprint of the AOL, producing mixed ocean–land waveforms. Conversely, if we can identify mixed ocean–land waveforms, the position of the OLI can be determined. This study aims to identify mixed infrared (IR) ocean–land waveforms and further proposes a novel method for determining the OLI using the identified mixed IR ocean–land waveforms. First, a novel fuzzy convolutional neural network is proposed to classify IR waveforms and output a predicted probability vector indicating the likelihood of the waveform being classified as either ocean or land waveforms. Second, this predicted probability vector is used to identify the mixed IR ocean–land waveforms. Finally, the position of the OLI is determined by using mixed IR ocean–land waveforms and the corresponding laser point clouds. The proposed method is applied to a raw AOL dataset collected via the Optech coastal zone mapping and imaging LiDAR system. Compared with the other traditional AOL-based OLI determination methods, the proposed mixed waveform method reduces the standard deviation of distance biases by 27.59% and improves the structural similarity index by 0.017. The low standard deviation and high structural similarity index indicate the effectiveness and correctness of the mixed waveform method for OLI determination via AOL.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681277","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248841","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

DET-YOLO: An Innovative High-Performance Model for Detecting Military Aircraft in Remote Sensing Images DET-YOLO：在遥感图像中探测军用飞机的创新型高性能模型

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-09-16 DOI: 10.1109/JSTARS.2024.3462745

Xiaoxin Chen;Hui Jiang;Hongxin Zheng;Jiankun Yang;Riqiang Liang;Dan Xiang;Hao Cheng;Zhansi Jiang

{"title":"DET-YOLO: An Innovative High-Performance Model for Detecting Military Aircraft in Remote Sensing Images","authors":"Xiaoxin Chen;Hui Jiang;Hongxin Zheng;Jiankun Yang;Riqiang Liang;Dan Xiang;Hao Cheng;Zhansi Jiang","doi":"10.1109/JSTARS.2024.3462745","DOIUrl":"10.1109/JSTARS.2024.3462745","url":null,"abstract":"To address the challenges of low detection rate and high missed detection rate of military aircraft in current complex remote sensing data, and to meet the requirements of real-time detection and easy deployment of models, this article introduces DET-you only look once (YOLO), an innovative detection model. First, to tackle the issue of reduced accuracy in identifying small targets amidst intricate backgrounds, a novel feature extraction component, C2f_DEF, was devised. This module replaced all existing C2f components within YOLOv8n, thereby significantly enhancing the model's ability to cope with complicated environmental contexts. Second, to achieve the functionality of easy deployment of the model, some deep structures were simplified to make the model more lightweight. Afterward, to further improve the model's ability to handle complex backgrounds and dense environments in remote sensing images and to improve the model's detection accuracy for military aircraft, the DAT module was embedded in the model. Finally, this article also optimized the loss function and reg_max to further reduce computational costs while improving the detection accuracy of the model. To verify the effectiveness and strong universality of DET-YOLO, extensive experimental verification was conducted on three publicly available datasets, namely MAR20, NWPU VHR-10, and NEU-DET. On the MAR20 dataset, compared with other advanced models, DET-YOLO achieved the highest mAP\u00000.5\u0000 (namely 94.7%) with only 80 training epochs while meeting lightweight and real-time requirements. While on the other two datasets, DET-YOLO also achieved the best detection performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681280","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248844","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