Spectral partitioning for hyperspectral remote sensing image classification
In this paper, we present a new approach for spectral partitioning which is intended to deal with ill-posed problems in hyperspectral image classification. First, we use adaptive affinity propagation (AAP) to intelligently group the original spectral bands. Such grouping strategy not only allows us to reduce the number of spectral bands, but also to provide a different perspective on the original hyperspectral data. Then, a multiple classifier system (MCS) based on multinomial logistic regression (MLR) is applied. The system is trained using different band subsets resulting from the previously conducted intelligent grouping, and the results are combined to produce a final classification result. Our experimental results, conducted using the well-known hyperspectral scenes collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Indian Pines region in NW Indiana, indicate that the proposed method can provide important advantages in terms of classification, in particular, when the number of training samples available a priori is very low.