An edge direction adaptive multi-scale watershed segmentation algorithm

Aiming at the problems of over-segmentation, insufficient segmentation and uncertainties of segmentation boundary in image segmentation, an edge direction adaptive multi-scale watershed segmentation algorithm for remote sensing images was proposed. This method mainly consisted of three steps. First, the gradient of each band was separately computed along twelve directions, and the largest value was taken as the final gradient. Then, gradient of many bands were composed according to the relevance of bands in each pixel. The gradient image was processed using the morphological reconstruction method to reduce the noise effects. At last, the image was segmented by the watershed algorithm using a multi-scale labelling strategy. The performance of proposed method was validated using three remote sensing images with different spatial resolutions, which are QuickBird, SPOT, Landsat TM, and compared with the methods of eCognition software, the traditional multi-bands watershed segmentation method and the morphological watershed segmentation method. The experimental result is extremely close to the actual ground boundary, and demonstrates that the proposed method is superior to the other three methods in terms of the edge matching percentage and the computational efficiency.