Interactive Hyperspectral Image Visualization Using Convex Optimization

In this paper, we propose a new framework to visualize hyperspectral images. We present three goals for such a visualization: 1) preservation of spectral distances; 2) discriminability of pixels with different spectral signatures; 3) and interactive visualization for analysis. The introduced method...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2009-06, Vol.47 (6), p.1673-1684
Main Authors: Ming Cui, Razdan, A., Jiuxiang Hu, Wonka, P.
Format: Article
Language:English
Subjects:
Applied geophysics
Displays
Earth sciences
Earth, ocean, space
Exact sciences and technology
Hyperspectral image visualization
Hyperspectral imaging
Image color analysis
Independent component analysis
Interactive
Internal geophysics
Laboratories
Linear programming
Monitoring
Optimization
perceptual color distances
Pixel
Pixels
Principal component analysis
principal component analysis (PCA)
Quantitative analysis
Spectra
Spectral signatures
Visualization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we propose a new framework to visualize hyperspectral images. We present three goals for such a visualization: 1) preservation of spectral distances; 2) discriminability of pixels with different spectral signatures; 3) and interactive visualization for analysis. The introduced method considers all three goals at the same time and produces higher quality output than existing methods. The technical contribution of our mapping is to derive a simplified convex optimization from a complex nonlinear optimization problem. During interactive visualization, we can map the spectral signature of pixels to red, green, and blue colors using a combination of principal component analysis and linear programming. In the results, we present a quantitative analysis to demonstrate the favorable attributes of our algorithm.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2008.2010129