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Research on Methods for Texture Feature-based Retrieval of Remote Sensing Image

Author: ChenZuoYong
Tutor: WangRenLi
School: PLA Information Engineering University
Course: Environmental Engineering
Keywords: Remote sensing image retrieval Grain Feature extraction Gray - gradient co-occurrence matrix Grayscale - smooth co-occurrence matrix Dual-tree complex wavelet
CLC: TP751
Type: Master's thesis
Year: 2008
Downloads: 77
Quote: 0
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Abstract


With the rapid development of aviation and aerospace technology , sensor technology , network technology , database technology , the acquired remote sensing image data is exponentially rapid growth at an alarming rate . How many large-scale remote sensing image database in a quick and efficient retrieval interested regional targets has become a bottleneck remote sensing image information extraction and sharing . Content-based image retrieval technology for the automatic extraction of remote sensing image the target area of interest to provide a powerful tool , has become a hot research . Texture features as the basic visual features , has been widely used in content-based remote sensing image retrieval . The main contents include: 1. Systematically elaborated and summarizes the content - based image retrieval involved in key technologies , including the description and content-based image feature extraction, feature similarity calculation as well as content-based image retrieval performance evaluation . 2 analysis and research for the deficiencies of the traditional co-occurrence matrix based on the co-occurrence matrix texture retrieval technology , will improve the co-occurrence matrix method for remote sensing image texture analysis , experiments show that the new method is robust to scale and rotation invariant . For wavelet algorithm using a new texture feature description method , the method more comprehensive description of the direction of the traditional wavelet transform , the experiment proved the feasibility and effectiveness of the algorithm . In conclusion , this paper pointed out the need for further study of the problem .

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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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