A generalized kernel for areal and intimate mixtures

In previous work, kernel methods were introduced as a way to generalize the linear mixing model for hyperspectral data. This work led to a new physics-based kernel that allowed accurate unmixing of intimate mixtures. Unfortunately, the new physics-based kernel did not perform well on linear mixtures...

Full description

Saved in:
Bibliographic Details
Published in2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing pp. 1 - 4
Main Authors Broadwater, Joshua, Banerjee, Amit
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
abundance estimation
areal mixtures
Atmospheric modeling
Hyperspectral imaging
intimate mixtures
Kernel
kernel functions
Materials
Physics
Pixel
Online AccessGet full text

Cover

More Information
Summary:In previous work, kernel methods were introduced as a way to generalize the linear mixing model for hyperspectral data. This work led to a new physics-based kernel that allowed accurate unmixing of intimate mixtures. Unfortunately, the new physics-based kernel did not perform well on linear mixtures; thus, different kernels had to be used for different mixtures. Ideally, a single unified kernel that can perform both unmixing of areal and intimate mixtures would be desirable. This paper presents such a kernel that can automatically identify the underlying mixture type from the data and perform the correct unmixing method. Results on real-world, ground-truthed intimate and linear mixtures demonstrate the ability of this new data-driven kernel to perform generalized unmixing of hyperspectral data.
ISBN:9781424489060
1424489067
ISSN:2158-6268
2158-6276
DOI:10.1109/WHISPERS.2010.5594962