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.
Segmentation of synthetic aperture radar (SAR) is a challenge topic in recent years. Many statistical and structural methods have been proposed for this goal. Some of them are based on clustering, such as the sparse spectral clustering and Nyström method. These methods suffer from the low speed and high computational complexity because of the use of the eigendecomposition in their algorithm. In this paper, we proposed an unsupervised feature learning method in which the features of different areas of SAR images are extracted, and then they will be learned using an unsupervised manner and finally the learned features will be clustered. The proposed algorithm improved the accuracy compared with other methods and it also has a shorter run time.
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