Novel Approaches for Spectral Unmixing of Hyperspectral Data

Abstract

This thesis addresses the problem of spectral unmixing of remotely sensed hyperspectral data. The spectral unmixing has the objective of quantifying the reectance properties of di_erent materials on the earth. Complete spectral unmixing includes estimating the number of constituent materials in the data, extracting their spectral signatures called endmembers, and estimating their contributing ground cover fractions called abundances from each location of the acquired scene. The need of spectral unmixing has been fostered with the development of powerful hyperspectral sensors in the field of remote sensing. These hyperspectral imagers acquire a set of co-registered images of a scene with hundreds of narrow and contiguous bands covering the visible, the near-infrared, and the mid-infrared wavelengths of the electromagnetic spectrum. Due to limited spatial (ground) resolution supported by the high altitude sensors, the materials are often spatially mixed. One can unmix them using algorithmic approaches without the need for spatial resolution enhancement. This thesis contributes new methodologies for decomposing the hyperspectral data into its constituent entities and also provides a unified framework for the complete spectral unmixing of the data. This kind of analysis yields myriad of data products in remote sensing, defense and military, agricultural development, urban planning, and in many other areas.