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

Remote Sensing

Yi Long²  Heng-Chao Li²  Turgay Celik⁴  Nathan Longbotham⁵  William J. Emery¹  Arko Lucieer³ 

[1] Department of Aerospace Engineering Sciences, University of Colorado, Boulder, CO 80309, USA; E-Mail:;Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu 610031, China; E-Mail:;Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu 610031, China; E-Mail;School of Computer Science, University of the Witwatersrand, Johannesburg 2000, South Africa; E-Mail:;DigitalGlobe, Inc., Longmont, CO 80503, USA; E-Mail:

关键词: hyperspectral image visualization;    dimensionality reduction;    multidimensional scaling;    human visual system;   

DOI  :  10.3390/rs70607785

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
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