Landcover change over central Virginia: comparison of endmember fractions in hyperspectral data

Stefanie Tompkins; Kellie McNaron-Brown; Jessica M. Sunshine; Jeffrey Burt

doi:10.1117/12.437039

Translator Disclaimer

20 August 2001 Landcover change over central Virginia: comparison of endmember fractions in hyperspectral data

Stefanie Tompkins, Kellie McNaron-Brown, Jessica M. Sunshine, Jeffrey Burt

Author Affiliations +

Proceedings Volume 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII; (2001) https://doi.org/10.1117/12.437039
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

A spectral mixture analysis (SMA) based change detection approach has been applied to hyperspectral image (HSI) data collected by the HyMap sensor. As a first step in extending this approach from multispectral to HSI data, an HSI change pair featuring a forested region in central Virginia in the fall of 1999 and 2000 was modeled via SMA as a linear combination of three main endmember materials: green vegetation, non-photosynthetic vegetation, and shade. The fractional abundance images resulting from the SMA are compared quantitatively to assess the level of detail with which change can be detected and understood from the HSI data. Alternatives to the simple three SMA endmember solution are discussed as well, including the use of additional endmembers to account for seasonal change or multiple vegetation species. The utility of SMA-based change detection for mapping subpixel changes in materials is demonstrated, as is the increased interpretability over traditional change detection approaches.

Citation Download Citation

Stefanie Tompkins, Kellie McNaron-Brown, Jessica M. Sunshine, and Jeffrey Burt "Landcover change over central Virginia: comparison of endmember fractions in hyperspectral data", Proc. SPIE 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, (20 August 2001); https://doi.org/10.1117/12.437039

ACCESS THE FULL ARTICLE