Augmenting a Hierarchical Classifier for Hyperspectral Data by Exploiting Spatial Correlation

Classification of hyperspectral data is a challenging problem because of the large dimensionality of data involved and non-binary nature of input classes. Most current methods do not consider the continuity of geographical features. In this work, augmenting the feature set of a pixel by appending data from its spatial neighborhood is experimentally seen to improve performance. Random projection achieves processing speedup with acceptable accuracy. Using both techniques together, we demonstrate an improvement in both accuracy and time characteristics of a binary hierarchical classifier.