Robust spatial-spectral hyperspectral image classification for vegetation stress detection

Hyperspectral imaging (HSI) techniques have been widely used for a variety of applications pertaining to vegetation species identification. With its rich spectral information, HSI is a powerful tool to detect and characterize vegetation species and their health. However, due to the high dimensionality of HSI, a the number of training samples required to estimate the parameters of the automated target recognition (ATR) or ground-cover classification algorithms is large. To avoid this over-dimensionality problem, feature selection or feature extraction must be performed to reduce the dimensionality of HSI data. This problem is further exacerbated when spatial information is also exploited in conjunction with spectral information. In this work, we propose a feature selection approach for extracting the most meaningful spatial and spectral features for a vegetative stress detection problem - genetic algorithms based linear discriminant analysis (GA-LDA). Experimental results show that applying GA with an appropriate fitness function in the spatial-spectral feature space is very effective at selecting the most pertinent features and yields very high classification accuracies.