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Remote Sensing Image Segmentation Base on Dual-Tree Complex Wavelet Transform and Gray-level Co-occurrence Matrix

Author: PanYing
Tutor: LiuXiaoDan
School: Liaoning Normal University
Course: Applied Computer Technology
Keywords: Tree complex wavelet transform GLCM Texture feature extraction Remote sensing image segmentation
CLC: TP751
Type: Master's thesis
Year: 2011
Downloads: 59
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Abstract


Segmentation as an important direction of development of the intelligent processing of the image , subject to a high degree of attention from the image processing community . Remote sensing image segmentation as an important application of image segmentation , by the researchers' attention . As remote sensing images compared with other types of images with multi- gray level , large amount of information , fuzzy boundaries , and there are synonyms spectrum \. However, with the earth observation satellite technology continues to mature , taken to remote sensing image texture information has become increasingly diverse , and therefore how remote sensing image segmentation using texture information to become one of the current concerns of scholars and texture features extraction is the basis of the subject . With remote sensing image texture analysis , extraction of remote sensing image texture features , can promote the automation of remote sensing image interpretation . Remote sensing image segmentation , texture analysis method with the conventional method of segmentation combination , help to improve the accuracy of the final segmentation of remote sensing images , which can be a better understanding of the remote sensing image , and a variety of useful topics extracted from the remote sensing data information . In this paper, based on a lot of reading literature on remote sensing image segmentation based on texture , put forward a new texture feature extraction methods , namely : remote sensing images combined to describe local texture tree complex wavelet transform and GLCM characteristics. This method uses tree complex wavelet high frequency modulus value the subband Gamma distribution and the Lognormal distribution parameter combination of characteristics , GLCM characteristics combined texture features of each pixel as a remote sensing image characteristics , then complete remote sensing image segmentation by K-means clustering . Experimental results show that the remote sensing image segmentation based on the texture feature extraction by this method are used to obtain a higher accuracy of segmentation .

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CLC: > Industrial Technology > Automation technology,computer technology > Remote sensing technology > Interpretation, identification and processing of remote sensing images > Image processing methods
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