Wavelet Packet Tree Pruning Metrics for Hyperspectral Feature Extraction

In this study, the authors investigate the use of the Wavelet Packet Decomposition (WPD) as a preprocessing stage for a multiclassifiers and decision fusion system used for hyperspectral automated target recognition (ATR). The hyperspectral signature is transformed using WPD, and each set of wavelet detail and approximation coefficients (terminal nodes or leaves on the WPD tree) are considered as feature vectors Dimensionality reduction and feature optimization is performed via WPD tree pruning, using ATR-appropriate pruning metrics such as class separation. The wavelet coefficients in the terminal nodes of the pruned tree are then used for form feature vectors. Three methods are then investigated: (i) treating each terminal node as independent feature vector that is input to an independent classifier in a multiclassifier decision fusion (MCDF) system, (ii) applying intelligent feature grouping to the terminal nodes, where each resulting group is treated as an independent feature vector that is input to an independent classifier in a MCDF system, (iii) concatenating all terminal nodes and applying stepwise linear discriminant analysis (SLDA) along with a single classifier. The efficacy of the proposed methods are investigated using an experimental hyperspectral database for a remote sensing agricultural application, namely early detection of the disease known as soybean rust (Phakopsora pachyrhizi) in soybean crops.