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Remote sensing data are primary sources for change detection because remote sensing technology can acquire Earth's surface feature in large area timely and simultaneously. This paper proposed a new change detection method base on spectral matching. Changes in spectra often correspond to changes in important surface physical parameters. Therefore, subtle land cover changes can be detected using spectral matching. In this paper, two Landsat ETM+ remote sensing data acquired in different year were used for change detection. Firstly, two imageries were precisely geometrically corrected. Secondly, two imageries were accurately atmospherically corrected, and pixel DN value converted into reflectance. Accuracy of change detection depends on accurate atmospheric correction. Thirdly, the spectral matching image was derived from two remote sensing dada using spectral angle mapper (SAM). Finally, change classification image was generated by density slice using certain thresholds value. Spectral matching technique is an effective tool for change detection.