A novel automated object identification approach using key spectral components

Spectral remote sensing provides solutions to a wide range of commercial, civil, agricultural, atmospheric, security, and defense problems. Technological advances have expanded multispectral (MSI) and hyperspectral (HSI) sensing capabilities from air and space borne sensors. The greater spectral and spatial sensitivity have vastly increased the available content for analysis. The amount of information in the data cubes obtained from today’s sensors enable material identification via complex processing techniques. With sufficient sensor resolution, multiple pixels on target are obtained and by exploiting the key spectral features of a material signature among a group of target pixels and associating the features with neighboring pixels, object identification is possible. The authors propose a novel automated approach to object classification with HSI data by focusing on the key components of an HSI signature and the relevant areas of the spectrum (bands) of surrounding pixels to identify an object. The proposed technique may be applied to spectral data from any region of the spectrum to provide object identification. The effort will focus on HSI data from the visible, near-infrared and short-wave infrared to prove the algorithm concept.