Study On Land Cover Classifiaction And Residential Areas Extraction Using Ikonos Imagery Based On Data Fusion

Land use classification based on high-resolution remote sensing images is a hot topic, but the research on residential areas extraction is still in preliminary stage, In this thesis, the high resolution IKONOS remote sensing image was took as the main data source, and the pixel-level fusion, feature-level fusion and decision-level fusion were respectively conducted for land cover classification and residential areas extraction. All of this was aimed to explore an efficient method for land cover classification and residential areas extraction with high resolution remote sensing image.Firstly, to prepare for the image fusion, land cover classification and residential areas extraction, the theory and method of remote sensing preprocessing, image enhancement processing and the remote sensing image feature extraction were analyzed.Secondly,the image pixel-level fusion methods were adopted the PCA, wavelet plus PCA, IHS and Brovey ,and the fusion result image was studied by the visual and quantitative estimate. Thirdly, based on the pixel-level IKONOS remote sensing fusion, we analyzed the spectral feature and texture feature of the fusion result image was analyzed, the feature-level fusion was conducted by the spectral feature, texture feature, spectral plus texture feature, and using SVM classifier,the residential areas was extracted. Then, land cover classification and residential areas extraction with combined texture feature was proposed by the sufficient analysis of the texture feature with different image window. Finally, the decision-level fusion was conducted to extract residential areas with multi-vote method and D-S evidence theory for the single classifier result of SVM, MLC and OO,and based on the analysis of decision-fusion result, the decision-level fusion of multi-vote method was improved to extract residential areas information.The pixel-level fusion result with IKONOS image indicated that the PCA fusion method can preferably improve the spatial resolution of multi-spectral image, at the same time maintaining the spectral information, and lower spectral distortion. The feature-level fusion result showed that it is better for land cover classification and residential areas extraction based on spectral information and feature-level fusion with combined texture feature than other image feature.,and improved the classification accuracy.And lastly the decision-level fusion result indicated that, compared with the three single classifiers'extraction result, the improved multi-vote decision-level fusion method achieved a better classification result, and the method achieved the purpose of residential areas extraction with decision-level fusion.