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Change Detection from Pre and PostUrbanisation LANDSAT 5TM Multispectral Images

Change detection (CD) from the Earth surface is a very attractive topic of research for the researchers contributing to the field of Earth observation and remote sensing. Texture classification from remotely sensed images is among one of the techniques to monitor these changes in a detailed manner. Grey Level Co-occurrence Matrix (GLCM) method for texture classification from remotely sensed satellite images provides us information about the second order statistics of the image in a detailed manner. It evaluates texture features (TF) of the image in terms of contrast, correlation, energy and homogeneity. These features not only predict the changing behaviour of the texture but also quantify these changes. This research examines the competence of post classification GLCM technique in the field of texture based change detection. Changing condition are analysed on the basis of pre and post-urbanisation Landsat 5TM images of Las Vegas (LV) for a period of around 25 years. Finally, the changing behaviour of the texture predicts the change of the Land-Cover (LC) in this period of time.