Multi-threshold SAR segmentation based on improved histogram PDE and two-dimensional Tsallis entropy

Due to the influence of synthetic aperture radar (SAR) imaging, there are a lot of speckle noises in the SAR image. The problem of segmentation and blurred edge is easy to appear in the segmentation of the SAR image. This paper presents a SAR image segmentation algorithm, which combines the improved histogram partial differential equation (PDE) and the two-dimensional Tsallis entropy multi threshold.The algorithm firstly combines the image denoising and the image enhancement weighting according to the PDE histogram equalization method, and uses the respective weights to adjust the denoising term and the image enhancement term to increase the image texture while denoising; the algorithm extends the 2D Tsallis entropy single threshold segmentation method to multi-threshold segmentation simultaneously, and establishes a selection method based on multiple thresholds; the algorithm also introduces the firefly algorithm to solve the optimal threshold pairs, and realizes the effective segmentation of denoising and enhancement SAR images with multi-threshold of two-dimensional Tsallis entropy.The simulation results show that compared with the other three segmentation algorithms, the proposed algorithm achieves higher segmentation accuracy when dealing with images with large noise and small gray-scale difference, which increases PRI by at least 2.53% and VOI by at least 8.48%, and reduced GCE by at least 11.14%.