¹Information Center of Natural Resources, Department of Natural Resources of Ningxia Hui Autonomous Region, Yinjiaqu North Street 25, Ningxia 750004, China
²Department of Geodesy and Geomatics Engineering, University of New Brunswick,15 Dineen Drive, P.O. Box 4400,FrederictonE3B 5A3, NB, Canada.

---

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 largeremote 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 borderscreated 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 thetraditional methodof dominant color similarity measure to make itmoresuitable 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 stitchingstrategy based on the similarity measure of dominant color can effectively eliminate artificial borders.

Index Terms—Large image segmentation,stitching algorithm,dominant color,similarity measure,dividing block

[PDF]

Cite: Haizhong Zhang, Ligang Wang, Fei Tong, "Large Remote Sensing Image Segmentation with Stitching Strategy Based on Dominant Color," Journal of Software vol. 16, no. 2, pp. 67-79, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)