Abstract: Aimed at the rough result and low computation speed of SAR image segmentation in wavelet domain, in this paper, a fast SAR image segmentation method based on chaotic particle swarm optimization algorithm and maximum Tsallis entropy in nonsubsampled contourlet transform (NSCT) domain is proposed. Firstly, the approximation and detail information are extracted using NSCT. And the approximation-detail gray level matrix is established. Then, the Tent map chaotic particle swarm optimization algorithm is introduced to search for the optimal threshold. The repeat computations of fitness function in iteration are reduced significantly with recursive algorithm. Experimental results show that, compared with the fast SAR image segmentation method in wavelet domain, the new method achieves better segmented result since the NSCT can extract information effectively owing to its multi-direction and shift invariance. And because the chaos is introduced and the fitness of particle is calculated using recursive method, the convergence accuracy is higher and the running time is less.