Pixel

Process (computing)

Biology

Contrast (vision)

Fishery

Operating system

Computer science

Geology

Satellite imagery

Pattern recognition (psychology)

Geography

Image (mathematics)

Multispectral image

Hyperspectral imaging

Abundance (ecology)

Artificial intelligence

Remote sensing

Abundance estimation

Endmember

Computer vision

In multispectral and hyperspectral image analysis for remote sensing, variations in contrast due to cloud shadows and topography can cause problems in the demixing process, creating false endmembers and erroneous fractional abundance images. This paper introduces a novel hyperspectral mixing model in which pixel contrast is accounted for explicitly in the image formation. A method is described for estimating the per-pixel contrast for any chosen endmember-based demixing algorithm. Applications of the method to both synthetic and real-world satellite imagery illustrate its efficacy.

A novel endmember, fractional abundance, and contrast model for hyperspectral imagery