Abstract
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.
Highlights
Classification is one of the most important applications of Polarimetric synthetic aperture radar (PolSAR) images, especially in urban areas [1,2,3]
The rich information obtained from the backscatter signal of the ground features and polarimetric target decomposition of the echo signals [4] contains the electromagnetic scattering characteristics of the target objects, and it is usually used in understanding the scattering characteristics [4] and classification of the ground features [5,6,7,8,9,10]
The PolSAR data is first initialized by the Cloude–Pottier decomposition [9], and the maximum likelihood classification is applied to extract the best-fit complex Wishart distribution [12] of the samples
Summary
Classification is one of the most important applications of Polarimetric synthetic aperture radar (PolSAR) images, especially in urban areas [1,2,3]. Entropy/Alpha/Anisotropy–Wishart is one of the most famous PolSAR classification methods [11] In this approach, the PolSAR data is first initialized by the Cloude–Pottier decomposition [9], and the maximum likelihood classification is applied to extract the best-fit complex Wishart distribution [12] of the samples. The PolSAR data is first initialized by the Cloude–Pottier decomposition [9], and the maximum likelihood classification is applied to extract the best-fit complex Wishart distribution [12] of the samples This method requires that the distribution of ground features follow a normal probability distribution function [13]. The complex distribution of ground features, especially for those in high-density urban area, often violates this premise and leads to poor classification results
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