TY - GEN
T1 - Unsupervised feature learning for scene classification of high resolution remote sensing image
AU - Fu, Min
AU - Yuan, Yuan
AU - Lu, Xiaoqiang
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/31
Y1 - 2015/8/31
N2 - 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.
AB - 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.
KW - high resolution image
KW - scene classification
KW - unsupervised feature learning
UR - http://www.scopus.com/inward/record.url?scp=84957589105&partnerID=8YFLogxK
U2 - 10.1109/ChinaSIP.2015.7230392
DO - 10.1109/ChinaSIP.2015.7230392
M3 - 会议稿件
AN - SCOPUS:84957589105
T3 - 2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings
SP - 206
EP - 210
BT - 2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015
Y2 - 12 July 2015 through 15 July 2015
ER -