A Comparative Performance Analysis of Feature Extraction Techniques for Hyperspectral Image Classification

Abstract

Obtaining thematic maps using image classification techniques from hyperspectral datasets is a very difficult image processing task. In hyperspectral image analysis dimensionality reduction is one of the challenging pre-processing tasks, which is achieved using feature extraction techniques. The beauty of these techniques is that they drastically reduces the dimensionality of image and at the same time preserves the majority of the essential information. In this paper few most frequently used dimensionality reduction methods are being investigated, which helps to get accurateness. This research work presents a relative performance investigation of few mostly frequently used feature extraction techniques like Decision Boundary Feature Extraction (DBFE), Non-Parametric Weighted Feature Extraction (NWFE), Discriminative analysis feature extraction (DAFE) and Principal Component Analysis (PCA). The classification is carried out using two most widely used classification techniques including Gaussian Maximum Likelihood (GML) and neural network (NNs). The results obtained after performing experiments indicates that Decision Boundary Feature Extraction (DBFE) technique has provided the best accuracy among various investigated feature extraction techniques. The application areas of this research include areas like identification of exact location in battle field, drought affected areas, flooded areas and weather forecasting etc.