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  4. 山地灾害与地表过程重点实验室
Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images

文献类型：期刊论文

作者Xu, Qingsong1,2; Yuan, Xin4; Ouyang, Chaojun1,2,5; Zeng, Yue3
刊名REMOTE SENSING
出版日期2020
卷号12期号:21页码:3501
关键词high-resolution and hyperspectral images spatial object distribution diversity spectral information extraction attention-based pyramid network heavy-weight spatial feature fusion pyramid network (FFPNet) spatial-spectral FFPNet
DOI10.3390/rs12213501
产权排序1
通讯作者Ouyang, Chaojun(cjouyang@imde.ac.cn)
文献子类Article
英文摘要Unlike conventional natural (RGB) images, the inherent large scale and complex structures of remote sensing images pose major challenges such as spatial object distribution diversity and spectral information extraction when existing models are directly applied for image classification. In this study, we develop an attention-based pyramid network for segmentation and classification of remote sensing datasets. Attention mechanisms are used to develop the following modules: (i) a novel and robust attention-based multi-scale fusion method effectively fuses useful spatial or spectral information at different and same scales; (ii) a region pyramid attention mechanism using region-based attention addresses the target geometric size diversity in large-scale remote sensing images; and (iii) cross-scale attention in our adaptive atrous spatial pyramid pooling network adapts to varied contents in a feature-embedded space. Different forms of feature fusion pyramid frameworks are established by combining these attention-based modules. First, a novel segmentation framework, called the heavy-weight spatial feature fusion pyramid network (FFPNet), is proposed to address the spatial problem of high-resolution remote sensing images. Second, an end-to-end spatial-spectral FFPNet is presented for classifying hyperspectral images. Experiments conducted on ISPRS Vaihingen and ISPRS Potsdam high-resolution datasets demonstrate the competitive segmentation accuracy achieved by the proposed heavy-weight spatial FFPNet. Furthermore, experiments on the Indian Pines and the University of Pavia hyperspectral datasets indicate that the proposed spatial-spectral FFPNet outperforms the current state-of-the-art methods in hyperspectral image classification.
电子版国际标准刊号2072-4292
WOS关键词SEMANTIC SEGMENTATION ; FUSION
资助项目NSFC[42022054] ; Strategic Priority Research Program of CAS[XDA23090303] ; National Key Research and Development Program of China[2017YFC1501000] ; CAS Youth Innovation Promotion Association
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000589272800001
出版者MDPI
资助机构NSFC ; Strategic Priority Research Program of CAS ; National Key Research and Development Program of China ; CAS Youth Innovation Promotion Association
源URL[http://ir.imde.ac.cn/handle/131551/50737]  
专题成都山地灾害与环境研究所_山地灾害与地表过程重点实验室
通讯作者Ouyang, Chaojun
作者单位1.Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Surface Proc, Chengdu 610041, Peoples R China;
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
3.Southwest Jiao Tong Univ, Sch Econ & Management, Chengdu 610031, Peoples R China
4.Bell Labs, Murray Hill, NJ 07974 USA;
5.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China;
推荐引用方式
GB/T 7714		 Xu, Qingsong,Yuan, Xin,Ouyang, Chaojun,et al. Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images[J]. REMOTE SENSING,2020,12(21):3501.
APA Xu, Qingsong,Yuan, Xin,Ouyang, Chaojun,&Zeng, Yue.(2020).Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images.REMOTE SENSING,12(21),3501.
MLA Xu, Qingsong,et al."Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images".REMOTE SENSING 12.21(2020):3501.

入库方式： OAI收割

来源：成都山地灾害与环境研究所

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