Large Remote Sensing Image Segmentation with Stitching Strategy Based on Dominant Color

Abstract

Large remote sensing image segmentation is a crucial issue in object-based image analysis. It is common sense that a segmentation framework consists of three components: (1) dividing large remote sensing image into blocks for overcoming the constraint of computer memory; (2) executing segmentation algorithm for each block individually; (3) stitching segmentation results of all blocks into a complete result for eliminating artificial borders created by dividing blocks. However, there is a lack of mature technologies to eliminate artificial borders produced by dividing blocks. In this paper, we proposed a new stitching strategy based on the dominant color similarity measure and modified the traditional method of dominant color similarity measure to make it more suitable for measuring the similarity of two segmented regions. A multi-scale segmentation algorithm is adopted for segmenting each block. External memory is used to store intermediate segmentation results and exchange data with internal memory. We tested the algorithm with three different images and validated that the algorithm can implement the segmentation for large remote sensing images in a common computer. Experiments demonstrate that the stitching strategy based on the similarity measure of dominant color can effectively eliminate artificial borders.