Unsupervised Classification of Fully Polarimetric SAR Image Based on Polarimetric Features and Spatial Features
Abstract: 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. Inthis paper, based on the scattering properties of fully
polarimetric SAR data, combing the statisticalcharacteristics and neighborhood
information, an efficient unsupervised method of fully polarimetric SARimage
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 total polarimetric power), 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 with clustering
algorithm. The experimental results show that better classification results can
be obtained in the Radarsat-2 data with the proposed method.
Keywords: Polarimetric SAR,
polarimetric feature, gray level co–occurrence matrix, image classification
Author: Xiaorong Xue
Journal Code: jptkomputergg160070
