A framework for the segmentation of high-resolution satellite imagery using modified seeded-region growing and region merging

Image segmentation is becoming increasingly important in areas such as object-oriented image classification in the field of remote-sensing image analysis. We present a new approach for the image segmentation of a high-resolution pan-sharpened satellite image based on modified seeded-region growing and region merging. First, we conduct some pre-processing prior to image segmentation to improve segmentation quality. The initial seeds are automatically selected using the proposed block-based seed-selection method. After automatic selection of significant seeds, initial segmentation is achieved by applying the modified seeded-region growing procedure. Finally, region merging, based on a region-adjacency graph, is carried out in post-processing to obtain the final segmentation result. Experimental results demonstrate that the proposed method shows better performance than other approaches, and has good potential for its application to the segmentation of high-resolution satellite imagery.