A framework for classification of urban areas using polarimetric SAR images integrating color features and statistical model

作者：Liu Hong-Ying^*; Wang Shuang; Wang Rong-Fang; Shi Jun-Fei; Zhang Er-Lei; Yang Shu-Yuan; Jiao Li-Cheng

来源：Journal of Infrared and Millimeter Waves, 2016, 35(4): 398-406.

DOI：10.11972/j.issn.1001-9014.2016.04.004

• polarimetric synthetic aperture radar (PoISAR)
• image classification
• color features
• extended scattering power entropy

The color features were exploited in a novel framework for the unsupervised classification of urban areas in this paper. Firstly, based on the recent four-component decomposition model of the polarimetric synthetic aperture radar (PoISAR) data, the common color spaces, such as YUV, RGB, HSI, and CIELab were calculated. The color feature was quantitatively selected from these color spaces by introducing the color entropy. Then together with the texture feature and the extended scattering power entropy, the adaptive mean-shift algorithm was used to segment the PoISAR data into clusters. Finally, the clusters were merged according to the GO distribution-based distance measurement. The proposed framework was verified by the experiments on one AIRSAR L-band and two Radarsat-2 C-band PoISAR data. The classification accuracy indicates that the proposed method has superior discriminative ability for urban areas compared with existing works.

• 出版日期2016-8
• 单位西安电子科技大学