A New Approach for Spatio-Spectral Feature Selection for Sensors with Noisy and Overlapping Spectral Bands

This paper extends a recently developed canonical-correlation feature-selection (CCFS) approach to a collective spatio-spectral feature-selection and classification framework for hyperspectral imagers. The work utilizes the concept of spectrally enhanced spatial features by integration of pixels´ spatial and spectral information. In order to determine the most informative features, the proposed methodology employs a sequential spatio-spectral feature-selection approach that consists of two distinct stages: a spatially independent spectral feature extraction, based on the CCFS, followed by a spatially enhanced classification. The performance of the new methodology is tested on target detection and classification applications using remotely sensed imagery collected by the Air-borne Hyperspectral Imager (AHI). Sensitivity of the spatio-spectral feature-selection approach with respect to the initial set of sensor bands and with respect to the number and types of spatial features utilized during the classification stage is also studied.