A New Statistical-Based Kurtosis Wavelet Energy Feature for Texture Recognition of SAR Images

Abstract

In this paper, an efficient algorithm for texture recognition of synthetic aperture radar (SAR) images is developed based on wavelet transform as a feature extraction tool and support vector machine (SVM) as a classifier. SAR image segmentation is an important step in texture recognition of SAR images. SAR images cannot be segmented successfully by using traditional methods because of the existence of speckle noise in SAR images. The algorithm, proposed in this paper, extracts the texture feature by using wavelet transform; then, it forms a feature vector composed of kurtosis value of wavelet energy feature of SAR image. In the next step, segmentation of different textures is applied by using feature vector and level set function. At last, an SVM classifier is designed and trained by using normalized feature vectors of each region texture. The testing sets of SAR images are segmented by this trained SVM. Experimental results on both agricultural and urban SAR images show that the proposed algorithm is effective for classification of different textures in SAR images, and it is also insensitive to the intensity.

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