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.
Highlights
Image segmentation divides images into partitions, which is typically used to recognize objects or other relevant information in digital images [1]
This paper presents a high-resolution remote sensing image segmentation algorithm based on improved quadtree structure and Region Adjacency Graph (RAG) technique
We proposed a fast method for standard deviation calculation method
Summary
Image segmentation divides images into partitions, which is typically used to recognize objects or other relevant information in digital images [1]. For processing of remote sensing image, especially for high-resolution image, image segmentation is a primary step in classification or other analysis. Object-oriented classification is a basic process for many applications including change detection [2] and land cover investigation [3]. High-resolution remote sensing images express more information of ground objects, and show great diversity of texture features. There exist several studies on object-oriented image segmentation. The most representative studies include Mean-Shift (MS) algorithm [5], a method based on kernel density estimation, which is widely used in data clustering analysis. It has been successfully applied to feature space analysis [6] and texture image segmentation [7]. The drawback of the MS algorithm is computational complexity for local maxima searching in the feature space. Some researchers propose using a speedup mechanism such as Locality-Sensitive Hashing, K-D
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