A clustering algorithm with optimized multiscale spatial texture information: application to SAR image segmentation
Authors: Tian, Xiaolin; Jiao, Licheng; Zhang, Xiaohua
Source: International Journal of Remote Sensing, Volume 34, Number 4, 2013 , pp. 1111-1126(16)
Publisher: Taylor and Francis Ltd
Abstract:An image segmentation method based on optimized spatial texture information is proposed in this article. Spatial information, including the relative position of neighbouring pixels and texture features of the multiscale neighbourhood, is incorporated into the similarity measure of the fuzzy c-means (FCM) clustering algorithm, in which the Gaussian kernel is adopted to diminish the local incorrect segmentation. The FCM clustering is spatially adjusted and optimized by the particle swarm optimization (PSO) algorithm. The purpose of optimization is to obtain the appropriate control parameters influencing spatial information, which can improve segmentation results. Experimental results demonstrate that the proposed method achieves better segmentation performance and is capable of effectively segmenting synthetic images and synthetic aperture radar (SAR) images.
Document Type: Research Article
Affiliations: Intelligent Perception and Image Understanding Key Lab of Ministry of Education of China,Institute of Intelligent Information Processing, Xidian University, Xi'an,710071, China
Publication date: February 20, 2013