Segmentation of high-resolution remotely sensed imagery combining spectral similarity with phase congruency
结合光谱相似性与相位一致模型的高分辨率遥感图像分割方法

A modified algorithm of marker-based watershed segmentation is proposed by combining spectral similarity with phase congruency model in this paper. The performance of segmentation using marker-based watershed algorithm is decided by the result of edge detection from remotely sensed imagery. Thus we use spectral similarity of the same type ground object from remotely sensed imagery to suppress fake edges and noises, with result that good segmentation results can be retrieved. In this paper, a spectral similarity model defined by the sum of distance of spectral curve between the target pixel and adjacent pixels is introduced into phase congruency model for edge detection. Then segmentation of remotely sensed imagery is obtained by using auto marker-based watershed algorithm. Finally, an unsupervised evaluation and comparison of the image segmentation from the proposed algorithm, the segmentation based on phase congruency model and some other existing algorithms is implemented using information entropy. Furthermore, the computation time of the proposed algorithm is also compared with other algorithms. The experimental segmentation results show that the proposed algorithm can reduce the over-segmentation phenomenon efficiently and is readily to obtain better segmentation results.