Abstract
In the process of object-oriented change detection, the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysis. Aiming at this problem, this paper presents a novel object-level change detection method based on multi-scale segmentation and fusion. First of all, the fine to coarse segmentation is used to obtain initial objects of different sizes; then, according to the features of the objects, Change Vector Analysis is used to obtain the change detection results of various scales. Furthermore, in order to improve the accuracy of change detection, this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusion. Based on these methods, experiments are done with SPOT5 multi-spectral remote sensing imagery. Compared with pixel-level change detection methods, the overall accuracy of our method has been improved by nearly 10%, and the experimental results prove the feasibility and effectiveness of the fusion strategies.
Highlights
Along with the rapid development of remote sensing data acquisition method and the gradual shortening of the acquisition cycle, its scope of application is becoming increasingly widespread and the application requirements are expanding
The remote sensing image data of the change detection paddy fields, bare land, buildings, roads, settlements and test used in this paper is the multi-spectral image of SPOT5 greenery
If in a district of Guangzhou city in 2006 and 2007. It contains the multi-scale segmentation scale is appropriate in the three bands of red, green and blue, with the resolution of process, the segmented polygons are able to draw a clear
Summary
Along with the rapid development of remote sensing data acquisition method and the gradual shortening of the acquisition cycle, its scope of application is becoming increasingly widespread and the application requirements are expanding. Many research scholars at home and abroad use optical remote sensing image, according to different changing detection target, a lot of methods and effective models are proposed (Su et al.,2011; Hazel G et al.,2001; Dai et al.,2012; Liang et al.,2013;Zhong et al.,2005;Wang et al.,2013;Sun et al.,2010). These methods can be roughly divided into 3 categories: pixel-level change detection, feature-level change detection, object-level change detection (Hazel G et al, 2001). How to objectively evaluate the results of the segmentation, obtain optimal segmentation scale and avoid the influence of subjective factors are becoming important (Chen et al, 2011)
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