Hyperspectral stimulated Raman scattering (SRS) imaging has rapidly become an emerging tool for high content analyses of cell and tissue systems. The label-free nature of SRS imaging combined with its chemical specificity allows in situ and in vivo biochemical quantification at submicrometer resolution without sectioning and staining. Current hyperspectral SRS data analysis methods are based on...

Hyperspectral imaging instruments are capable of collecting hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. One of the main problems in the analysis of hyperspectral data cubes is the presence of mixed pixels, which arise when the spatial resolution of the sensor is not enough to separate spectrally distinct materials. Hyperspec...

Mixed pixels, which are inevitable in remote sensing images, often result in a lot of limitations in their applications. A novel approach for mixed pixel’s fully constrained unmixing, Fully Constrained Oblique Subspace Projection (FCOBSP) Linear Unmixing algorithm, is proposed to handle this problem. The Oblique Subspace Projection, in which the signal space is oblique to the background space, ...

This study examined linear spectral unmixing techniques for mapping the variation in crop yield for precision agriculture. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery collected from a grain sorghum field and a cotton field. A pair of crop plant and soil spectra derived from each image was used as endmember spectra to generate...

A snow-cover mapping method accounting for forests (SnowFrac) is presented. SnowFrac uses spectral unmixing and endmember constraints to estimate the snow-cover fraction of a pixel. The unmixing is based on a linear spectral mixture model, which includes endmembers for snow, conifer, branches of leafless deciduous trees and snow-free ground. Model input consists of a land-cover fraction map and...

The Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location of affected areas. Due to the high temporal variability of flood events, Earth Observation (EO) data ...