SAR Image Segmentation With Rényi's Entropy

Synthetic aperture radar (SAR) image segmentation is an important task in image processing. However, classic segmentation techniques are inadequate due to the presence of speckle noise. In this paper, we present a methodology for SAR image segmentation that uses the matrix of Rényi's entropy. This matrix arises from SAR data that follows the GA0model, and here, it is an input to segmentation methods. For performance evaluation of the proposed methodology, we employ the error of segmentation, the cross-region fitting index, the Dice measure, as well as the rates of false positives and negatives. Tests have been performed on synthetic and real SAR images and the matrix of entropy has improved the results, regardless of the increase of the number of looks. The Otsu's method of global thresholding produced good segmentation results when applied to this matrix.