Segmentation parameter estimation algorithm Based on curvelet transform coefficients energy for feature extraction and texture description of SAR images

Synthetic aperture radar (SAR) image processing has many applications in the fields of target recognition, mineral detection, weather forecasting, agricultural, and etc. due to its high spatial resolution and imaging technology. However, the process of this type of images is difficult because of the existence of speckle noise. Nowadays, segmentation of textural regions based on designing a Kernel function with proper parameters is a real challenge. In this paper, a new parameter estimation algorithm has been proposed to design an efficient Kernel function for texture-based segmentation of SAR images. In this method, the Curvelet transform is applied to the SAR image only in one step and the inner layer coefficients as texture features are extracted. Then, a kernel function is formed based on the kurtosis value of the Curvelet coefficients energy (KCE). In the next step, the segmentation of different textures is applied by using the estimated KCE Kernel function. Experimental results on both simulated and real SAR images demonstrate that the proposed algorithm is effective for segmentation and description of different textures in SAR images, and it contains less misclassified pixels in comparison with other methods.