Utilization of local and global hyperspectral features via wavelet packets and multiclassifiers for robust target recognition

In this study, the authors investigate the combination of the wavelet packet decomposition (WPD) and multiclassifiers and decision fusion (MCDF) for a robust hyperspectral classification system. The authors investigate the use of the WPD multiresolution feature grouping and selection, forming groups of local and global spectral features, where each group is input to a classifier, resulting in local and global classifications. Then the labels are fused to form one class label. The classification system was applied to hyperspectral data for an agricultural application, namely the detection of different soybean rust infestation levels. The system was compared to current state-of-the-art hyperspectral analysis techniques to determine its comparative efficacy as compared to more conventional approaches, such as stepwise-linear discriminant analysis (LDA) or discriminant analysis feature extraction (DAFE) and current state-of-the art approaches, like spectral-domain multiclassifiers and decision fusion (MCDF). The proposed system had a classification accuracy which was approximately 40% higher than the SLDA approach and approximately 15% higher than MCDF.