Hyperspectral image classification using band selection and morphological profile

In this paper, we propose a new methodology to combine spectral information and spatial features for Support Vector Machine (SVM)-based classification. The novelty of the proposed work is in the combination of band selection (i.e., linear prediction (LP)-based method), spatial feature extraction (i.e., morphology profiles (MP)), and spectral transformation (i.e., principal component analysis (PCA)) to build a computationally tractable system. The preliminary result with ROSIS data shows that using the selected bands and MP features extracted from principal components (PCs) can yield the highest accuracy. We believe such finding is instructive to feature extraction/selection for spectral/spatial-based hyperspectral image classification.