A Two-Phase Algorithm Based on Kurtosis Curvelet Energy and Unsupervised Spectral Regression for Segmentation of SAR Images

Abstract

Texture-based segmentation of synthetic aperture radar (SAR) image is a difficult task in remote sensing applications because it must address the problem of speckle noise. Several methods have been proposed for this purpose based on clustering, but suffer from long run times, computational complexity, and high-memory consumption. The proposed technique consists of two phases for SAR image segmentation. A new algorithm for parameter estimation based on curvelet coefficient energy (KCE) to design an optimum kernel function and an unsupervised spectral regression (USR) method have been proposed in phases 1 and 2, respectively, for SAR image segmentation. Eigen-decomposition is not required in USR, which decreases run times over other methods. The proposed algorithm uses a single-stage curvelet to extract the texture feature. Then, a new term is introduced based on the kurtosis feature value of the curvelet coefficients energy of the SAR image. Finally, the level set method is used to outline the boundaries between textures. Subimages are then extracted from the textures. After the Gabor filter is applied and the features are extracted, they are learnt using USR and clustered using a $k$ -means algorithm. It is demonstrated that the clustering results based on the learned features will be improved significantly. SAR image segmentation is performed using the $k$ -means after applying the Gabor filter bank and feature extraction. The results of segmentation are compared with Nystrom and parallel sparse spectral clustering (PSSC). The proposed method was shown to be more accurate and had a shorter run time than either Nystrom or PSSC.