中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 西安光学精密机械研究所
  3. 中国科学院西安光学精密机械研究所
  4. 光学影像学习与分析中心
热门
Remote Sensing Scene Classification by Unsupervised Representation Learning

文献类型：期刊论文

作者Lu, Xiaoqiang; Zheng, Xiangtao; Yuan, Yuan; Lu, XQ
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2017-09-01
卷号55期号:9页码:5148-5157
关键词Adaptive Deconvolution Network Remote Sensing Scene Classification Unsupervised Representation Learning
ISSN号0196-2892
DOI10.1109/TGRS.2017.2702596
产权排序1
文献子类Article
英文摘要With the rapid development of the satellite sensor technology, high spatial resolution remote sensing (HSR) data have attracted extensive attention in military and civilian applications. In order to make full use of these data, remote sensing scene classification becomes an important and necessary precedent task. In this paper, an unsupervised representation learning method is proposed to investigate deconvolution networks for remote sensing scene classification. First, a shallow weighted deconvolution network is utilized to learn a set of feature maps and filters for each image by minimizing the reconstruction error between the input image and the convolution result. The learned feature maps can capture the abundant edge and texture information of high spatial resolution images, which is definitely important for remote sensing images. After that, the spatial pyramid model (SPM) is used to aggregate features at different scales to maintain the spatial layout of HSR image scene. A discriminative representation for HSR image is obtained by combining the proposed weighted deconvolution model and SPM. Finally, the representation vector is input into a support vector machine to finish classification. We apply our method on two challenging HSR image data sets: the UCMerced data set with 21 scene categories and the Sydney data set with seven land-use categories. All the experimental results achieved by the proposed method outperform most state of the arts, which demonstrates the effectiveness of the proposed method.
学科主题Geochemistry & Geophysics
WOS关键词SATELLITE IMAGES ; TOPIC MODEL ; FEATURES ; NETWORK ; FUSION
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000408346600024
资助机构National Basic Research Program of China (Youth 973 Program)(2013CB336500) ; State Key Program of National Natural Science of China(60632018 ; National Natural Science Foundation of China(61472413) ; Chinese Academy of Sciences(KGZD-EW-T03 ; Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences(LSIT201408) ; Young Top-notch Talent Program of Chinese Academy of Sciences(QYZDB-SSW-JSC015) ; 61232010) ; QYZDB-SSW-JSC015)
源URL[http://ir.opt.ac.cn/handle/181661/29243]  
专题西安光学精密机械研究所_光学影像学习与分析中心
通讯作者Lu, XQ
作者单位Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPTicallMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714		 Lu, Xiaoqiang,Zheng, Xiangtao,Yuan, Yuan,et al. Remote Sensing Scene Classification by Unsupervised Representation Learning[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2017,55(9):5148-5157.
APA Lu, Xiaoqiang,Zheng, Xiangtao,Yuan, Yuan,&Lu, XQ.(2017).Remote Sensing Scene Classification by Unsupervised Representation Learning.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,55(9),5148-5157.
MLA Lu, Xiaoqiang,et al."Remote Sensing Scene Classification by Unsupervised Representation Learning".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 55.9(2017):5148-5157.

入库方式： OAI收割

来源：西安光学精密机械研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。