TY - GEN
T1 - Feature extraction for PolSAR image classification using multilinear subspace learning
AU - Tao, Mingliang
AU - Zhou, Feng
AU - Su, Jia
AU - Xie, Jian
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - Multiple informative polarimetric descriptors can be computed from direct measurements of polarimetric covariance matrix and target decomposition theorems. Under the tensor algebra framework, each pixel is modeled as a third-order tensor object by combining multi-features and incorporating neighborhood spatial information together. Typically, the tensor object is of high correlation and redundancy in both the spatial and feature dimensions. In this paper, we propose a feature extraction method using the multilinear principal component analysis to facilitate the classification process. Experimental results in comparison with principal component analysis, independent component analysis and linear discriminate analysis demonstrate that the classification accuracy is significantly improved since the extracted features by the proposed method are more discriminative.
AB - Multiple informative polarimetric descriptors can be computed from direct measurements of polarimetric covariance matrix and target decomposition theorems. Under the tensor algebra framework, each pixel is modeled as a third-order tensor object by combining multi-features and incorporating neighborhood spatial information together. Typically, the tensor object is of high correlation and redundancy in both the spatial and feature dimensions. In this paper, we propose a feature extraction method using the multilinear principal component analysis to facilitate the classification process. Experimental results in comparison with principal component analysis, independent component analysis and linear discriminate analysis demonstrate that the classification accuracy is significantly improved since the extracted features by the proposed method are more discriminative.
KW - feature extraction
KW - land cover classification
KW - multilinear subspace learning
KW - Polarimetric synthetic aperture radar (PolSAR)
UR - http://www.scopus.com/inward/record.url?scp=85041808066&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2017.8127324
DO - 10.1109/IGARSS.2017.8127324
M3 - 会议稿件
AN - SCOPUS:85041808066
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1796
EP - 1799
BT - 2017 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Y2 - 23 July 2017 through 28 July 2017
ER -