Class-oriented spectral partitioning for hyperspectral image classification
This paper presents a new approach for class-oriented spectral partitioning for hyperspectral image classification. First, without empirical information, we automatically search the spectral bands that correspond to a specific class by using different band selection approaches. Then, the obtained class-oriented spectral partitions are used respectively as the input of a group of classifiers, the results of which are combined together to generate a final one by a multiple classifier system. Our experimental results, conducted with the well-known Indians Pines test site hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) in NW Indiana, suggest that our presented spectral partitioning method leads to competitive results when compared with other state-of-the-art approaches.