IMPROVEMENT OF THERMAL ESTIMATION AT LAND COVER BOUNDARY BY USING QUANTILE

Abstract. Land cover classification was conducted for Landsat ETM image of Urmqi. Maximum likelihood classification algorism was used for this purpose. Classification classes were urban, water body, forest, soil, bare ground1, bare ground2, vegetation1, vegetation2 and vegetation3. Mask image of each land cover was created from the obtained classification image. Thermal band image of each land cover was extracted by using the mask image. In general, mean value and standard deviation are calculated for the thermal band image. However, these values were affected by the difference of ground resolution. In this study, we introduced quantiles to avoid this problem. Quantiles are points taken at regular intervals from the cumulative distribution function. Quantiles showed the effectiveness of decreasing the error caused from the difference of ground resolution.