Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm
Year: 2016
Type: article
Abstract: Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm.The rich spectral information captured by hyperspectral sensors has given rise to a number of remote sensing applications, ranging from vegetative assessment and crop health monitoring, to military surveillance and combatant identification.However, due to limited spatial resolution, multiple ground materials generally contribute, i.e. mix, to form the spectrum recorded for a single pixel.The unmixing problem considers the inverse problem of determining the underlying material spectra, called endmembers, from sensor measurements.While classical unmixing approaches were deterministic in nature and did not attempt to identify in-scene materials, recent methods use labeled training data to generate statistical models of endmember variabilities and perform statistical unmixing for simultaneous material identification and abundance estimation.However, the computational complexity of statistical unmixing is O(N 3 ), cubic in the
Author: Charan Puladas
Institution: Wright State University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2481222072