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[SCI]Improved Class-Specific Codebook with Two-Step Classification for Scene-Level Classification of High Resolution Remote Sensing Images

时间:2017-05-03 来源:未知 点击:
作者:Yan, L (Yan, Li)[ 1 ] ; Zhu, RX (Zhu, Ruixi)[ 1 ] ; Mo, N (Mo, Nan)[ 1 ] ; Liu, Y (Liu, Yi)[ 1 ]
REMOTE SENSING
: 9 : 3 文献号: 223
DOI: 10.3390/rs9030223
出版年: MAR 2017
摘要
With the rapid advances in sensors of remote sensing satellites, a large number of high-resolution images (HRIs) can be accessed every day. Land use classification using high-resolution images has become increasingly important as it can help to overcome the problems of haphazard, deteriorating environmental quality, loss of prime agricultural lands, and destruction of important wetlands, and so on. Recently, local feature with bag-of-words (BOW) representation has been successfully applied to land-use scene classification with HRIs. However, the BOW representation ignores information from scene labels, which is critical for scene-level land-use classification. Several algorithms have incorporated information from scene labels into BOW by calculating a class-specific codebook from the universal codebook and coding a testing image with a number of histograms. Those methods for mapping the BOW feature to some inaccurate class-specific codebooks may increase the classification error. To effectively solve this problem, we propose an improved class-specific codebook using kernel collaborative representation based classification (KCRC) combined with SPM approach and SVM classifier to classify the testing image in two steps. This model is robust for categories with similar backgrounds. On the standard Land use and Land Cover image dataset, the improved class-specific codebook achieves an average classification accuracy of 93% and demonstrates superiority over other state-of-the-art scene-level classification methods.
关键词
作者关键词:scene-level land use classificationBag-of-words (BOW)improved class-specific codebookkernel collaborative representative based classification combined with SPMtwo-step classification
KeyWords Plus:LEARNING ALGORITHMSRECOGNITIONFEATURESSELECTIONSCALE
作者信息
通讯作者地址Liu, Y (通讯作者)

http://images.webofknowledge.com/WOKRS524B8/images/zh_CN/expand.gif Wuhan Univ, Sch Geodesy & Geomat, 129 Luoyu Rd, Wuhan 430079, Peoples R China.
地址:
http://images.webofknowledge.com/WOKRS524B8/images/zh_CN/expand.gif [ 1 ] Wuhan Univ, Sch Geodesy & Geomat, 129 Luoyu Rd, Wuhan 430079, Peoples R China
电子邮件地址:lyan@sgg.whu.edu.cnruixzhu@whu.edu.cnnmo@whu.edu.cnyliu@sgg.whu.edu.cn
基金资助致谢

基金资助机构 授权号
Non-profit Industry Financial Program of the Ministry of Land and Resources  20151100901 
查看基金资助信息   
出版商
MDPI AG, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
类别 / 分类
研究方向:Remote Sensing
Web of Science 类别:Remote Sensing
文献信息
文献类型:Article
语种:English
入藏号: WOS:000398720100037
ISSN: 2072-4292
期刊信息
·         Impact Factor (影响因子): 3.036