Unsupervised feature learning for scene classification of high resolution remote sensing image

Min Fu, Yuan Yuan, Xiaoqiang Lu

科研成果: 书/报告/会议事项章节会议稿件同行评审

7 引用 (Scopus)

摘要

Due to the rapid development of various satellite sensors, a large amount of high resolution remote sensing images can be obtained. In order to efficiently represent the scenes from these high resolution images, an unsupervised feature learning method is proposed for high resolution image scene classification. In the proposed method, a set of filter banks are learned in an unsupervised manner from the unlabeled image patches, which are robust, efficient and do not need elaborately designed descriptors such as SIFT. And then, each image is encoded by these filter banks using a soft distance assignment scheme, generating a final feature vector to excellently represent the image scene. Finally, by virtue of the traditional SVM classifier, the sematic concepts of different scenes can be categorized. Experimental evaluation on the the high resolution remote sensing images demonstrates the effectiveness and good performance of the proposed method.

源语言英语
主期刊名2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
206-210
页数5
ISBN(电子版)9781479919482
DOI
出版状态已出版 - 31 8月 2015
已对外发布
活动IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Chengdu, 中国
期限: 12 7月 201515 7月 2015

出版系列

姓名2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings

会议

会议IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015
国家/地区中国
Chengdu
时期12/07/1515/07/15

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