TY - JOUR
T1 - Interactive hyperspectral image visualization using convex optimization
AU - Cui, Ming
AU - Razdan, Anshuman
AU - Hu, Jiuxiang
AU - Wonka, Peter
N1 - Funding Information:
Manuscript received July 7, 2008; revised September 16, 2008 and October 21, 2008. First published April 14, 2009; current version published May 22, 2009. This work was supported in part by the National Geospatial-Intelligence Agency under Grants HM1582-08-BAA-0003 and HM1582-05-1-2004 and by the National Science Foundation under Grant IIS 0612269.
PY - 2009/6
Y1 - 2009/6
N2 - 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.
AB - 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.
KW - Hyperspectral image visualization
KW - Linear programming
KW - Perceptual color distances
KW - Principal component analysis (PCA)
UR - http://www.scopus.com/inward/record.url?scp=67349143233&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67349143233&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2008.2010129
DO - 10.1109/TGRS.2008.2010129
M3 - Article
AN - SCOPUS:67349143233
SN - 0196-2892
VL - 47
SP - 1673
EP - 1684
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 6
M1 - 4814563
ER -