Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data
Michael W. Matthew, S. M. Adler‐Golden, Alexander Berk, Gerald W. Felde, Gail P. Anderson, D. Gorodetzky, S. E. Paswaters, Margaret Shippert
Abstract
Michael W. Matthew, S. M. Adler‐Golden, Alexander Berk, Gerald W. Felde, Gail P. Anderson, D. Gorodetzky, S. E. Paswaters, Margaret Shippert
Abstract
With its combination of good spatial and spectral resolution, visible to near infrared spectral imaging from aircraft or spacecraft is a highly valuable technology for remote sensing of the Earth's surface. Typically it is desirable to eliminate atmospheric effects on the imagery, a process known as atmospheric correction. We review the basic methodology of first-principles atmospheric correction and present results from the latest version of the FLAASH (fast line-of-sight atmospheric analysis of spectral hypercubes) algorithm. We show some comparisons of ground truth spectra with FLAASH-processed AVIRIS (airborne visible/infrared imaging spectrometer) data, including results obtained using different processing options, and with results from the ACORN (atmospheric correction now) algorithm that derive from an older MODTRAN4 spectral database.
OpenAlex reports 79 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
With its combination of good spatial and spectral resolution, visible to near infrared spectral imaging from aircraft or spacecraft is a highly valuable technology for remote sensing of the Earth's surface. Typically it is desirable to eliminate atmospheric effects on the imagery, a process known as atmospheric correction. We review the basic methodology of first-principles atmospheric correction and present results from the latest version of the FLAASH (fast line-of-sight atmospheric analysis of spectral hypercubes) algorithm. We show some comparisons of ground truth spectra with FLAASH-processed AVIRIS (airborne visible/infrared imaging spectrometer) data, including results obtained using different processing options, and with results from the ACORN (atmospheric correction now) algorithm that derive from an older MODTRAN4 spectral database.
Key concepts: Atmospheric correction, Remote sensing, Computer science, Algorithm, Reflectivity, Geology, Physics, Optics