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Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique
Institute of Geomatics, Department of Civil Engineering, Tsinghua University, Chengfu Road, Beijing 100084, China
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Received: 28 April 2013; in revised form: 21 June 2013 / Accepted: 23 June 2013 / Published: 5 July 2013
Abstract: An approach based on the improved quadtree structure and region adjacency graph for the segmentation of a high-resolution remote sensing image is proposed in this paper. In order to obtain the initial segmentation results of the image, the image is first iteratively split into quarter sections and the quadtree structure is constructed. In this process, an improved fast calculation method for standard deviation of image is proposed, which significantly increases the speed of quadtree segmentation with standard deviation criterion. A spatial indexing structure was built using improved Morton encoding based on this structure, which provides the merging process with data structure for neighborhood queries. Then, in order to obtain the final segmentation result, we constructed a feature vector using both spectral and texture factors, and proposed an algorithm for region merging based on the region adjacency graph technique. Finally, to validate the method, experiments were performed on GeoEye-1 and IKONOS color images, and the segmentation results were compared with two typical algorithms: multi-resolution segmentation and Mean-Shift segmentation. The experimental results showed that: (1) Compared with multi-resolution and Mean-Shift segmentation, our method increased efficiency by 3–5 times and 10 times, respectively; (2) Compared with the typical algorithms, the new method significantly improved the accuracy of segmentation.
Keywords: image segmentation; remote sensing imagery; quadtree; spatial indexing; region adjacency graph
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Fu, G.; Zhao, H.; Li, C.; Shi, L. Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique. Remote Sens. 2013, 5, 3259-3279.
Fu G, Zhao H, Li C, Shi L. Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique. Remote Sensing. 2013; 5(7):3259-3279.
Fu, Gang; Zhao, Hongrui; Li, Cong; Shi, Limei. 2013. "Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique." Remote Sens. 5, no. 7: 3259-3279.