There have always been some challenges within the remote sensing community related to the processing of contiguous spectral bands contained in hyperspectral datasets. Most approaches would resort to using averaged spectral information over wide bandwidths resulting in loss of crucial information available in those contiguous bands. The loss of information could mean a drop in the discriminative power when it comes to land cover classes with comparable spectral responses, as in the case of cultivated fields versus fallow lands. In this study, we proposed and tested three optimized novel algorithms based on Moment Distance Index (MDI) that characterizes the whole shape of the spectral curve. The image classification tests conducted on two publicly available hyperspectral data sets (AVIRIS 1992 Indian Pine and HYDICE Washington DC Mall images) showed the robustness of the optimized MDI algorithms in terms of classification accuracy. We achieved an overall accuracy of 98% and 99% for AVIRIS and HYDICE, respectively using the optimized MDI algorithms. The optimized indices were also time efficient as it avoided the process of band dimension reduction, such as those implemented by several well-known classifiers. Our results showed the potential of the optimized shape indices to discriminate between grass/pasture and grass/trees, tree and grass, and between types of tillage (corn-min and corn-notill) under object-based random forest approach. The results highlight the importance of MDI that completely utilizes the contiguous spectral bands to define the gradient of the curve and improve image classification accuracy.