王越宾等:Self-Supervised Low-Rank Representation (SSLRR) for Hyperspectral Image Classification
被阅读 49 次
2018-11-12
Self-Supervised Low-Rank Representation (SSLRR) for Hyperspectral Image Classification
作者:Wang, YB (Wang, Yuebin)[ 1 ] ; Mei, J (Mei, Jie)[ 1 ] ; Zhang, LQ (Zhang, Liqiang)[ 1 ] ; Zhang, B (Zhang, Bing)[ 2 ] ; Li, AJ (Li, Anjian)[ 1 ] ; Zheng, YB (Zheng, Yibo)[ 1 ] ; Zhu, PP (Zhu, Panpan)[ 1 ]
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷: 56  期: 10  页: 5658-5672
DOI: 10.1109/TGRS.2018.2823750
出版年:OCT 2018
 
摘要
Low-rank representation (LRR) can construct the relationships among pixels for hyperspectral image (HSI) classification with a given dictionary and a noise term. However, the accuracy of HSI classification based on LRR methods is degraded with the redundant and noise information existed in pixels. The neglect of semantic information around pixels in the LRR methods may cause "salt-and-pepper" problem in HSI classification. To avoid the aforementioned problems, a novel self-supervised low-rank representation method called SSLRR is developed. In SSLRR, the LRR and spectral-spatial graph regularization are developed as the pixel-level constraints to remove the redundant and noise information in HSIs. Superpixel constraints including data structure and relationship construction are further utilized to provide supervised feedback information to the subspace learning to avoid the "salt-and-pepper" problem generated in the pixel-based classification methods, and simultaneously enhance the performance of LRR. The pixel-level and superpixel-level regularizations are explicitly integrated into a unified objective function for LRR. By means of the linearized alternating direction method with adaptive penalty, the solution to the objective function is achieved by employing a customized iterative algorithm. We perform comprehensive evaluation of the proposed method on three challenging public HSI data sets. We obtain new state-of-the-art performance on these data sets, and achieve improvements of 44.3%, 13.4%, and 30.1% in overall accuracy compared to the best LRR method.
 
作者信息
通讯作者地址: Zhang, LQ (通讯作者)
Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.
地址:
 
[ 1 ] Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China