Use of PPI and n-dimensional visualizer in identifying and classifying the purest spectral pixels and curves by ASTER data (case study: southwest Ardestan, Isfahan)

Many commonly used spectral image analysis techniques are based on the fact that remotely sensed imagery is sampled with numerous spectral bands at narrow bandwidths, making it possible to construct a spectrum for each pixel in the image. For identify and classify the most pure pixels and spectral curves, the n-dimensional visualizer is used after performing the Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The group of pixels in the corners of the scatter plot can be separated from other cloud data and selected as end-members corresponding to a particular type of minerals, rocks, or any individual phenomenon. Referring to the actual location of these pixels in the image, the end-member spectrum is extracted. Therefore, this process was performed after calibration of Internal Average Relative Reflection (IARR) on the ASTER dataset in southwest Ardestan, Isfahan. The geological units of the study area mainly consist of clay units (such as shale), carbonate rocks (calcite) and vegetation. This process resulted in the extraction of three end-member including illite, calcite and the green vegetation from the image.