Parallel unsupervised Synthetic Aperture Radar image change detection on a graphics processing unit

Authors:
Huming Zhu;Yu Cao;Zhiqiang Zhou;Maoguo Gong;Licheng Jiao
Affiliations:
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, China;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, China
Venue:
International Journal of High Performance Computing Applications
Year:
2013

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Abstract

Change detection is now routinely applied in many application domains, such as damage assessment, environmental monitoring and agricultural surveys. As the number of remote sensing images and the complexity of algorithms rise, the demand for processing power is increasing. In this paper, we propose PLog-FLICM , a parallel algorithm for change detection, which includes two steps: (1) generate the difference image based on the log-ratio operator; (2) detect changes in the difference image by using a modified fuzzy c-means clustering algorithm. PLog-FLICM is implemented on AMD Accelerated Parallel Processing SDK based on Open Computing Language. The parallel characteristics and implementation details of the proposed PLog-FLICM algorithm are presented. Experiments on several Synthetic Aperture Radar images demonstrate that the proposed algorithm outperforms other algorithms, and the designed parallel algorithm can greatly reduce the computational time of the change detection algorithm. Furthermore, we investigate the performance portability of PLog-FLICM in the different central processing unit and graphics processing unit platforms. Experimental results show that they have also achieved good parallel performance.