Application of Decision Tree Method in Remote Sensing Geological Mapping

The decision tree is an effective and accurate method in remote sensing classification. We use this method to classify the remote sensing data-ETM+ (enhanced thematic mapper plus), which covers most of the area of Minhe County, Qinghai Province. The acquisition date of ETM+ is October 29, 1999. We get digital elevation model (DEM) data from (1:) 250 000 topography map of Minhe area. The format of DEM is MapInfo exchange format *.mif which converted to (geography) coordinate. After primary treatment of the raw data, the DEM data is derived from the contour line. The ETM+ scenes are rectified using the DEM and sun-illumination model. NDVI and other three indexes from Tasseled Cap transform were calculated from RS data. All these indexes are stacked with five RS bands. The target objects are selected. The sampling of the target object is based on visual observation. First, false color composed imagery is essential, and the sampling process is interactive. The digital information of the target object is derived from the program, which compiled by IDL. The decision tree model was calculated by Clementine7.2 software suite. About 10% raw data were used to validate the accuracy of the model. Meantime others were used to build the model. Ten iterative numbers and 75% trim radio are the suit parameters for this model. Then we get the most suitable layer and numerical value for distinguishing different target objects. For instance, distinguishing Tertiary red clastic and loess's best layer is band 1 and band 3. In the next step, we import the model to Envi 4.0 and classify the raw data into different target objects. After some basic treatments, for example clump and (assign) class color, we get the final result map. The map is contrasted with 1:250 000 geology map of Minhe area and achieved the accuracy of classification. The result is that the decision tree method is better than traditional classify methods. Another conclusion is that the rules from decision tree could help geologists to gain a appropriate geological conclusion.