Fast graph-based SAR image segmentation via simple superpixels

Biao Hou, Xiaohua Zhang, Dezhao Gong, Shuang Wang, Xiangrong Zhang, Licheng Jiao

Abstract: Graph-based methods have been successfully applied in the field of computer vision for image segmentation. Unfortunately, most of them are not suitable to deal with large-scale SAR image segmentation due to their high computation complexity. A fast and efficient graph-based SAR image segmentation is proposed in this paper through using superpixels to reduce the computation complexity. Firstly, a SAR image is divided into several non-overlapped subdivisions with the same size. Each of the subdivision is processed as a single OpenMP parallel region, which can be processed at the single computing node with multi-core CPU. Secondly, the number of nodes and edges in the graph is reduced by extracting the superpixels other than single pixels based on global information of each subdivision. Finally, an effective rule is proposed to merge two adjacent sub-graphs from two different subdivisions into a new subgraph.