An iterative classification approach for mapping natural resources from satellite imagery

This project explores an iterative classification process as an alternative to two-stage classifications. In the iterative classification approach cover types are classified, one or two at a time, and the band selection process is repeated in each iteration, so that the combination of bands that provides the best separability among the classes that remain to be classified is selected. The optimum number of bands to perform the classification is also determined for each iteration, so that the classification of the area that is masked in each iteration achieves the highest possible accuracy. Spectral Pattern Analysis, and Spectral Separability Indices are used in the band selection process. GIS analysis is used to obtain prior probabilities, and to determine if variables such as elevation, slope, and aspect can result in a source of information for segmentation of the study area into more homogeneous strata. The results of this study show that: (1) The proposed iterative classification approach is superior to traditional single-step supervised and unsupervised approaches with a 99 per cent confidence level, and (2) Prior probabilities improve band selection process only when the TD separability index is used, but do not improve the classification process itself. GIS analysis of the study area may serve as a very useful tool for segmenting the study area into more homogeneous strata, but due to the large size of the study area, and the large number of classes (21 classes) being discriminated it did not help in the classification performed in this study.