Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Ways to Explore Information Content of Hyperspectral Remote Sensing Data

Not Accessible

Your library or personal account may give you access

Abstract

Modern hyper- and ultra- spectral remote sensors are capable of providing spectra with thousands of channels. These channels are not independent of each other. We will analyze the information content of the hyperspectral data using principal component analysis. We will show that the information content of the original spectrum is conserved by Empirical Orthogonal Function (EOF) transformations. A radiative transfer model and a physical inversion algorithm based on principal component analysis will be presented.

© 2009 Optical Society of America

PDF Article
More Like This
Exploring Information Content of Hyperspectral Remote Sensing Data

Xu Liu, W. Wu, Q. Yang, D. Zhou, A. Larar, and B. Wielicki
HW2C.3 Hyperspectral Imaging and Sounding of the Environment (HISE) 2018

Radiative Transfer Modeling and Retrievals for Advanced Hyperspectral Sensors

X. Liu, D. K. Zhou, A. M. Larar, W. L. Smith, and Stephen A. Mango
HMC2 Hyperspectral Imaging and Sounding of the Environment (HISE) 2007

Advanced Radiative Models and Retrieval Algorithms for Hyperspectral Remote Sensing Data

Xu Liu
HT2B.1 Hyperspectral Imaging and Sounding of the Environment (HISE) 2015

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved