The improved spectral resolution of modern hyperspectral sensors provides a means for discriminating subtly different classes of on ground materials in remotely sensed images. However, in order to obtain statistically reliable classification results, the number of necessary training samples can increase exponentially as the number of spectral bands increases. Obtaining the necessary number of training signals for these high-dimensional datasets may not be feasible. The problem can be overcome by preprocessing the data to reduce the dimensionality and thus reduce the number of required training samples. In this thesis, three dimensionality reduction methods, all based on parametric projection pursuits, are investigated. These methods are the Sequential Parametric Projection Pursuits (SPPP), Parallel Parametric Projection Pursuits (PPPP), and Projection Pursuits Best Band Selection (PPBBS). The methods are applied to very high spectral resolution data to transform the hyperspectral data to a lower-dimension subspace. Feature extractors and classifiers are then applied to the lower-dimensional data to obtain target detection accuracies. The three projection pursuit methods are compared to each other, as well as to the case of using no dimensionality reduction preprocessing. When applied to hyperspectral data in a precision agriculture application, discriminating sicklepod and cocklebur weeds, the results showed that the SPPP method was optimum in terms of accuracy, resulting in a classification accuracy of >95% when using a nearest mean, maximum likelihood, or nearest neighbor classifier. The PPPP method encountered optimization problems when the hyperspectral dimensionality was very high, e.g. in the thousands. The PPBBS method resulted in high classification accuracies, >95%, when the maximum likelihood classifier was utilized; however, this method resulted in lower accuracies when the nearest mean or nearest neighbor classifiers were used. When using no projection pursuit preprocessing, the classification accuracies ranged between ~50% and 95%; however, for this case the accuracies greatly depended on the type of classifier being utilized.

This thesis investigates the use of sequential parametric projection pursuits (SPPP) for hyperspectral dimensionality reduction and invasive species target recognition. The SPPP method is implemented in a top-down fashion, where hyperspectral bands are used to form an increasing number of smaller groups, with each group being projected onto a subspace of dimensionality one. Both supervised and unsupervised potential projections are investigated for their use in the SPPP method. Fisher?s linear discriminant analysis (LDA) is used as a potential supervised projection. Average, Gaussian-weighted average, and principal component analysis (PCA) are used as potential unsupervised projections. The Bhattacharyya distance is used as the SPPP performance index. The performance of the SPPP method is compared to two other currently used dimensionality reduction techniques, namely best spectral band selection (BSBS) and best wavelet coefficient selection (BWCS). The SPPP dimensionality reduction method is combined with a nearest mean classifier to form an automated target recognition (ATR) system. The ATR system is tested on two invasive species hyperspectral datasets: a terrestrial case study of Cogongrass versus Johnsongrass and an aquatic case study of Waterhyacinth versus American Lotus. For both case studies, the SPPP approach either outperforms or performs on par with the BSBS and BWCS methods in terms of classification accuracy; however, the SPPP approach requires significantly less computational time. For the Cogongrass and Waterhyacinth applications, the SPPP method results in overall classification accuracy in the mid to upper 90?s.