SAR image change detection based on Spark-FLICM algorithm

Huming Zhu, Yuqi Guo, Mingwei Niu, Linyan Qiu, Licheng Jiao, Maoguo Gong

Abstract: In this paper, we propose a Spark-based fuzzy local information C-Means (FLICM) algorithm that provides synthetic aperture radar (SAR) image change detection. With the volume and resolution of SAR images increasing, current serial clustering algorithms are not suitable to handle big data, scalable solutions are indispensable. The proposed algorithm based on Spark framework implements FLICM algorithm according to MapReduce programming model. The membership degree is obtained in parallel during Map phase where the local information is obtained by broadcasting arrays and clustering centers are obtained during Reduce phase. With a cluster composed of five nodes, experiments on real SAR images show good performance.