Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area

Remote Sensing

Lei Deng²  Ya-nan Yan¹  Chen Sun²  Jixian Zhang²  Nicolas Baghdadi² 

[1] College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;

关键词: polarimetric SAR;    sub-aperture decomposition;    polarimetric decomposition;    decision tree;   

DOI  :  10.3390/rs70201380

来源: mdpi

【 摘 要 】

A novel approach is proposed for classifying the polarimetric SAR (PolSAR) data by integrating polarimetric decomposition, sub-aperture decomposition and decision tree algorithm. It is composed of three key steps: sub-aperture decomposition, feature extraction and combination, and decision tree classification. Feature extraction and combination is the main contribution to the innovation of the proposed method. Firstly, the full-resolution PolSAR image and its two sub-aperture images are decomposed to obtain the scattering entropy, average scattering angle and anisotropy, respectively. Then, the difference information between the two sub-aperture images are extracted, and combined with the target decomposition features from full-resolution images to form the classification feature set. Finally, C5.0 decision tree algorithm is used to classify the PolSAR image. A comparison between the proposed method and commonly-used Wishart supervised classification was made to verify the improvement of the proposed method on the classification. The overall accuracy using the proposed method was 88.39%, much higher than that using the Wishart supervised classification, which exhibited an overall accuracy of 69.82%. The Kappa Coefficient was 0.83, whereas that using the Wishart supervised classification was 0.56. The results indicate that the proposed method performed better than Wishart supervised classification for landscape classification in urban area using PolSAR data. Further investigation was carried out on the contribution of difference information to PolSAR classification. It was found that the sub-aperture decomposition improved the classification accuracy of forest, buildings and grassland effectively in high-density urban area. Compared with support vector machine (SVM) and QUEST classifier, C5.0 decision tree classifier performs more efficient in time consumption, feature selection and construction of decision rule.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.