TY - GEN
T1 - Robust PCANet for hyperspectral image change detection
AU - Yuan, Zhenghang
AU - Wang, Qi
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2018 IEEE
PY - 2018/10/31
Y1 - 2018/10/31
N2 - Deep learning is an effective tool for handling high-dimensional data and modeling nonlinearity, which can tackle the hyperspectral data well. Usually deep learning methods need a large number of training samples. However, there is no labeled data for training in change detection (CD). Considering these, this paper develops an unsupervised Robust PCA network (RPCANet) for hyperspectral image CD task. The main contributions of this work are twofold: 1) An unsupervised convolutional neural networks named RPCANet is proposed to handle the hyperspectral image CD; 2) An effective CD framework using the RPCANet and change vector analysis (CVA) is designed to achieve better CD performance with more powerful features. Experimental results on real hyperspectral data sets demonstrate the effectiveness of the proposed method.
AB - Deep learning is an effective tool for handling high-dimensional data and modeling nonlinearity, which can tackle the hyperspectral data well. Usually deep learning methods need a large number of training samples. However, there is no labeled data for training in change detection (CD). Considering these, this paper develops an unsupervised Robust PCA network (RPCANet) for hyperspectral image CD task. The main contributions of this work are twofold: 1) An unsupervised convolutional neural networks named RPCANet is proposed to handle the hyperspectral image CD; 2) An effective CD framework using the RPCANet and change vector analysis (CVA) is designed to achieve better CD performance with more powerful features. Experimental results on real hyperspectral data sets demonstrate the effectiveness of the proposed method.
KW - Change detection (CD)
KW - Change vector analysis (CVA)
KW - Hyperspectral image
KW - Robust PCA network (RPCANet)
UR - http://www.scopus.com/inward/record.url?scp=85063123631&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8518196
DO - 10.1109/IGARSS.2018.8518196
M3 - 会议稿件
AN - SCOPUS:85063123631
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4931
EP - 4934
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -