Abstract
Abstract. Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA) on the accuracy and slightly inferior to FNEA on the efficiency.
Highlights
Image segmentation is the premise of object-oriented high resolution remote sensing image analysis
Since the advantage of remote sensing image object-oriented information extraction is more and more obvious, many scholars has been done a lot of research on the issues of multiple features fusion, multi-scale and multi-temporal high resolution remote sensing image segmentation
Literature(Wu Zhao cong,2013) proposed a comprehensive utilization of spectrum, texture and shape information segmentation method. It could effectively improve the quality of segmentation for the relatively rich areas of the texture information comparing with software eCognition fractal network evolution algorithm methods, but the method was not combined with the feature of edge information and context and so on
Summary
Image segmentation is the premise of object-oriented high resolution remote sensing image analysis. Accurate segmentation determines the success or failure of subsequent remote sensing image processing. Image segmentation is that the images dividing into several connected regions do not overlap each other according to certain rules. The algorithms are going based on the two basic characteristics of general brightness values, namely discontinuity and similarity. The first kind of method is based on the brightness of the discontinuous changes, such as the edge of the image, etc. Other is dividing images into the similar area according to the standards set, the threshold processing, for examples, threshold processing, regional growth, region separation and aggregation(Rafael C G,2002). With the enlargement of the application domain, image segmentation gradually becomes the key technology in the field of remote sensing image processing, pattern recognition and computer vision
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