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Article

Pairwise-Distance-Analysis-Driven Dimensionality Reduction Model with Double Mappings for Hyperspectral Image Visualization

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Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu 610031, China
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College of Big Data & Information Engineering Sciences, Guizhou University, Guiyang 550025, China
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School of Computer Science, University of the Witwatersrand, Johannesburg 2000, South Africa
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DigitalGlobe, Inc., Longmont, CO 80503, USA
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Department of Aerospace Engineering Sciences, University of Colorado, Boulder, CO 80309, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Arko Lucieer and Prasad S. Thenkabail
Remote Sens. 2015, 7(6), 7785-7808; https://doi.org/10.3390/rs70607785
Received: 29 January 2015 / Accepted: 2 June 2015 / Published: 12 June 2015
This paper describes a novel strategy for the visualization of hyperspectral imagery based on the analysis of image pixel pairwise distances. The goal of this approach is to generate a final color image with excellent interpretability and high contrast at the cost of distorting a few pairwise distances. Specifically, the principle of equal variance is introduced to divide all hyperspectral bands into three subgroups and to ensure the energy is distributed uniformly between them, as in natural color images. Then, after detecting both normal and outlier pixels, these three subgroups are mapped into three color components of the output visualization using two different mapping (i.e., dimensionality reduction) schemes for the two types of pixels. The widely-used multidimensional scaling (MDS) is used for normal pixels and a new objective function, taking into account the weighting of pairwise distances, is presented for the outlier pixels. The pairwise distance weighting is designed such that small pairwise distances between the outliers and their respective neighbors are emphasized and large deviations are suppressed. This produces an image with high contrast and good interpretability while retaining the detailed information content. The proposed algorithm is compared with several state-of-the-art visualization techniques and evaluated on the well-known AVIRIS hyperspectral images. The effectiveness of the proposed strategy is substantiated both visually and quantitatively. View Full-Text
Keywords: hyperspectral image visualization; dimensionality reduction; multidimensionalscaling; human visual system hyperspectral image visualization; dimensionality reduction; multidimensionalscaling; human visual system
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MDPI and ACS Style

Long, Y.; Li, H.-C.; Celik, T.; Longbotham, N.; Emery, W.J. Pairwise-Distance-Analysis-Driven Dimensionality Reduction Model with Double Mappings for Hyperspectral Image Visualization. Remote Sens. 2015, 7, 7785-7808. https://doi.org/10.3390/rs70607785

AMA Style

Long Y, Li H-C, Celik T, Longbotham N, Emery WJ. Pairwise-Distance-Analysis-Driven Dimensionality Reduction Model with Double Mappings for Hyperspectral Image Visualization. Remote Sensing. 2015; 7(6):7785-7808. https://doi.org/10.3390/rs70607785

Chicago/Turabian Style

Long, Yi, Heng-Chao Li, Turgay Celik, Nathan Longbotham, and William J. Emery 2015. "Pairwise-Distance-Analysis-Driven Dimensionality Reduction Model with Double Mappings for Hyperspectral Image Visualization" Remote Sensing 7, no. 6: 7785-7808. https://doi.org/10.3390/rs70607785

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