<![CDATA[Classification percentage of impervious surface area is a technique for classifying the percentage of impervious contained in an image. The purpose to determine how much impervious surface in examined area. The image used is satellite image from Landsat-7 ETM+. The method used in this design is Principal Component Regression (PCR). PCR will optimize the variables that have an important role in the formation of the model classifier. The input used is the value of pixel grayscale of each image and also the values of NDVI and NDBI. The output of the program in the form of thematic maps and impervious percentage of the image under study. However, the percentange of error from the program is not known, because there is no accurate data on the percentage of impervious. Key Words:PCR, Impervious Surface Area, Regression, Remote Sensing.]]>
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