In this dissertation, methods are designed and validated for the utilization of hyperspectral hypertemporal remotely sensed data in target detection applications. Two new classes of methods are designed to optimize the selection of target detection features from spectro-temporal space data. The first method is based on the consideration that all the elements of the spectro-temporal map are independent of each other. The second method is based on the consideration that the elements of the spectro-temporal map have some vicinal dependency among them. Methods designed for these two approaches include various stepwise selection methods, windowing approaches, and clustering techniques. These techniques are compared to more traditional feature extraction methods such as Normalized Difference Vegetation Index (NDVI), spectral analysis, and Principal Component Analysis (PCA). The efficacies of the new methods are demonstrated within an aquatic invasive species detection application, namely discriminating waterhyacinth from other aquatic vegetation such as American lotus. These two aquatic plant species are chosen for testing the proposed methods as they have very similar physical characteristics and they represent a practical life target detection problem. It is observed from the overall classification accuracy estimates that the proposed feature extraction methods show a marked improvement over conventional methods. Along with improving the accuracy estimates, these methods demonstrate a capability to drastically reduce the dimensionality while retaining the desired hyperspectral hypertemporal features. Furthermore, the feature set extracted using the newly developed methods provide information about the optimum subset of the hyperspectral hypertemporal data for a specific target detection application, which makes these methods serve as tools to strategize more intelligent data collection plans.