TY - GEN
T1 - An efficient classification method of fully polarimetric SAR image based on polarimetric features and spatial features
AU - Xue, Xiaorong
AU - Di, Liping
AU - Guo, Liying
AU - Lin, Li
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/9
Y1 - 2015/9/9
N2 - Polarimetric SAR(PolSAR) has played more and more important roles in earth observation. Polarimetric SAR image classification is one of the key problems in the PolSAR image interpretation. In this paper, based on the scattering properties of fully polarimetric SAR data, combing the statistical characteristics and neighborhood information, an efficient method of fully polarimetric SAR image classification is proposed. In the method, polarimetric scattering characteristics of fully polarimetric SAR image is used, and in the denoised total power image of polarimetric SAR, Span, the texture features of gray level co-occurrence matrix are extracted at the same time. Finally, the polarimetric information and texture information are combined for fully polarimetric SAR Image classification by clustering algorithm. The experimental results show that better classification results can be obtained in the Radarsat-2 data with the proposed method.
AB - Polarimetric SAR(PolSAR) has played more and more important roles in earth observation. Polarimetric SAR image classification is one of the key problems in the PolSAR image interpretation. In this paper, based on the scattering properties of fully polarimetric SAR data, combing the statistical characteristics and neighborhood information, an efficient method of fully polarimetric SAR image classification is proposed. In the method, polarimetric scattering characteristics of fully polarimetric SAR image is used, and in the denoised total power image of polarimetric SAR, Span, the texture features of gray level co-occurrence matrix are extracted at the same time. Finally, the polarimetric information and texture information are combined for fully polarimetric SAR Image classification by clustering algorithm. The experimental results show that better classification results can be obtained in the Radarsat-2 data with the proposed method.
KW - gray level co-occurrence matrix
KW - image classification
KW - polarimetric feature
KW - Polarimetric SAR
UR - http://www.scopus.com/inward/record.url?scp=84960392338&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84960392338&partnerID=8YFLogxK
U2 - 10.1109/Agro-Geoinformatics.2015.7248090
DO - 10.1109/Agro-Geoinformatics.2015.7248090
M3 - Conference contribution
AN - SCOPUS:84960392338
T3 - 2015 4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015
SP - 327
EP - 331
BT - 2015 4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015
Y2 - 20 July 2015 through 24 July 2015
ER -