Multiclassifiers and decision fusion in the wavelet domain for exploitation of hyperspectral data

In this paper, the discrete wavelet transform (DWT) is employed as a preprocessing stage for a multiclassifier and decision fusion system for feature extraction and dimensionality reduction of hyperspectral data. As a result, both global and local spectral features can be exploited. Feature grouping is conducted according to wavelet decomposition levels, or scales. Each DWT decomposition level’s detail coefficients are classified independently, creating a multiclassifer system. The resulting classifications are then fused using a simple majority voting scheme. The proposed target recognition system was applied to hyperspectral data for an agrictultural applications, namely detecting the presence of the often devastating disease known as soybean rust in soybean crops. The proposed approach was compared to well-known hyperspectral dimensionality reduction methods, such as stepwise linear discriminant anlaysis (LDA). When using the DWT multiclassifier system, the overall classification accuracies ranged from the high 80’s to the mid 90’s. When using the stepwise LDA technique the overall classification accuracies ranged from the mid 60s to the mid 90’s.