Post Classification Comparison Change Detection of GuangZhou Metropolis, China

摘要

Change detection is one of the most important applications of remote sensing techniques due to its capability of repetitive acquisition imageries with consistent image quality, at short intervals, on a global scale, and during complete seasonal cycles. This paper uses two Landsat ETM+ imageries acquired in 2000 and 2002 respectively to detect change of Guangzhou in southern China during two years using post classification comparison method. Firstly, two remote sensing data are precision geometrically corrected to UTM projection with a root mean square error (RMSE) of 0.3 pixels, and then they are classfied using Maximum Likelihood method respectively. Images are classified into four classes which are water, forest, grass or crop and building, soil or unused land. Sencondly, two classified images are calculated by band geometric algorithm pixel by pixel using programming. The class value of pixel in different year is the same, and then the processed pixel is zero, whereas the processed pixel is assigned to a certain value which represents change from the one land cover type to another during two years. Finally, statistic analyses of change information during two years are computed and the post classification comparison change detection image is outputted. It concludes that the largest change areas are exchanges of building, soil or unused land with grass land, and land covers in Baiyun district are changed mostly from 2000 to 2002.