Spectral Information Adjustment Using Unsharp Masking and Bayesian Classifier for Automatic Building Extraction from Urban Satellite Imagery

Building extraction in remote sensing images of urban areas is based on various classification techniques, demands development of various image processing and pattern recognition algorithms. Current techniques have poor performances in low local contrast conditions and require preprocessing methods for improving local contrast. In this novel approach, Unsharp Masking [USM] and Motion based Unsharp Masking [MUSM] methods are introduced to increase the local contrast in class images. In the proposed classification techniques, wherever spatial relationships drawn from buildings are imperative, the structural pattern recognition is properly utilized. In very high resolution remote sensing images where, the Bayesian classifier performs recognition of very small building and other cluttered areas, USM techniques are essential in amplifying the high frequency components of the original image which is used for building discrimination. The novelty of this paper is performing preprocessing technique which modifies frequency components of satellite image. In order to benchmark the algorithm, some of the Google Earth three bands (RGB) images were used. It is comprehend able from the results that the accuracy of small and large building classification using unsharp masking technique increases as compared with the methods without any preprocessing steps. [Seyed Mostafa Mirhassani, Bardia Yousefi, Alireza Moghaddamjoo. Automatic Building Extraction from Urban Satellite Imagery Using Bayesian Classifier and Unsharp Masking as Spectral Information. Journal of American Science 2012;8(1):554-564]. (ISSN: 1545-1003). http://www.americanscience.org. 77