Brightness gradient-corrected hyperspectral image mosaics for fractional vegetation cover mapping in northern California. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Jänicke, Clemens; Okujeni, Akpona; Cooper, Sam; Clark, Matthew; Hostert, Patrick; van der Linden, Sebastian
• Is Part Of: Remote sensing letters. Volume 11:Issue 1(2020)
• Page Start: 1
• Page End: 10
• Publication Date: 2020-01-02
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2019.1670518
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: We evaluated the effectiveness of different approaches to compensate for across-track brightness gradients within a hyperspectral image mosaic comprised of multiple flight lines in the San Francisco Bay Area. We calculated the spectral consistency of adjacent flight lines and conducted regression-based unmixing of woody- and non-woody vegetation fractions to assess the comparative benefits of the methods. Results showed that a class-wise empirical approach produced the most spectrally consistent, nearly seamless image mosaics and led to accurate vegetation fraction maps (mean absolute error = 12.6%). Overall, a class-wise empirical approach is recommended as a simple, flexible and transferable technique to compensate for brightness gradients over a global empirical approach, brightness normalization or continuum removal.