The Research Of Remote Sensing Feature Extraction

With the development of remote sensing technology, remote sensing images have been widely utilized in industry, agriculture and military affairs. Remote sensing classification is very important to all these applications. Now, many features and classifiers have been proposed. The extraction of efficient features and the selection of classifiers are pivotal for classification.This thesis employs texture features for remote sensing classification. The contents of this thesis could be summarized as follow. First, it introduces the definition of traditional statistical texture features such as: co-occurrence features, gray-level difference features, run-length features, Tamura features and gray-level information features. Based on the criterion of variances between & intra classes efficient features have been chosen among the extracted features. Secondly, The Gabor filter with the ability of simulating the biological vision has been used for texture features extraction. After the definition of Gabor filter and construction method, this thesis constructs series of Gabor filters with strong ability for classification. Spectrum histogram features has been applied to describe texture information of images processed by Gabor filters. Lastly, the thesis does some research on nearest neighbor classifiers and neural network classifiers and the experiment demonstrates that Gabor filter combined with spectrum histogram features yield higher accuracy than traditional statistical texture features.