Dimensionality reduction based on binary encoding for hyperspectral data

ABSTRACTBinary encoding is an approach that aims at summarizing the information contained in various spectral bands into a single image that stores the meaningful information of the bands. In this paper, it is introduced a feature extraction approach to reduce the dimensionality of hyperspectral data with binary encoding for classification purposes. Different options to reduce the radiometric information of the pixels are introduced, such as using a single threshold or multiple thresholds. After the dimensionality reduction, the separation of the spectral classes was analysed and the thematic classification of the reduced data was performed. In order to evaluate the performance of the proposed approach, experiments on AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) image, ROSIS (Reflection Optics System Imaging Spectrometer) hyperspectral image and HYDICE (Hyperspectral Digital Imagery Collection Experiment) hyperspectral image are presented. In the experiments, neighbouring spectral bands are gro...