Optimizing land use classification using decision tree approaches

Abstract

Supervised classification is one of the important tasks in remote sensing image interpretation, in which the image pixels are classified to various predefined land use/land cover classes based on the spectral reflectance values in different bands. In reality some classes may have very close spectral reflectance values that overlap in feature space. This produces spectral confusion among the classes and results in inaccurate classified images. To remove such spectral confusion one requires extra spectral and spatial knowledge. This report presents a decision tree classifier approach to extract knowledge from spatial data in form of classification rules using Gini Index and Shannon Entropy (Shannon and Weaver, 1949) to evaluate splits. This report also features calculation of optimal dataset size required for rule generation, in order to avoid redundant Input/output and processing.