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.