TY - GEN
T1 - Joint collaborative representation for polarimetric SAR image classification
AU - Geng, Jie
AU - Fan, Jianchao
AU - Wang, Hongyu
AU - Fu, Anyan
AU - Hu, Yuanyuan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - Polarimetric synthetic aperture radar (PolSAR) images are widely applied in terrain and ground cover classification. Feature extraction and classifier design are both important in Pol- SAR image classification. In this paper, various target decompositions are applied to obtain different polarimetric features. Since that neighboring pixels usually belong to the same species, they can be simultaneously represented through linear combinations of training samples. Therefore, a collaborative representation-based classifier with spatially joint regularization is adopted for classification. Experimental results demonstrate that the joint collaborative representation model performs better than other state-of-the-art methods, such as support vector machine and simultaneous sparse representation.
AB - Polarimetric synthetic aperture radar (PolSAR) images are widely applied in terrain and ground cover classification. Feature extraction and classifier design are both important in Pol- SAR image classification. In this paper, various target decompositions are applied to obtain different polarimetric features. Since that neighboring pixels usually belong to the same species, they can be simultaneously represented through linear combinations of training samples. Therefore, a collaborative representation-based classifier with spatially joint regularization is adopted for classification. Experimental results demonstrate that the joint collaborative representation model performs better than other state-of-the-art methods, such as support vector machine and simultaneous sparse representation.
KW - Classification
KW - collaborative representation
KW - polarimetric synthetic aperture radar (PolSAR)
KW - spatial regularization
UR - http://www.scopus.com/inward/record.url?scp=85007413334&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2016.7729793
DO - 10.1109/IGARSS.2016.7729793
M3 - 会议稿件
AN - SCOPUS:85007413334
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3066
EP - 3069
BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Y2 - 10 July 2016 through 15 July 2016
ER -