Hyperspectral image visualization using t-distributed stochastic neighbor embedding

Biyin Zhang; Xin Yu

doi:10.1117/12.2205840

14 December 2015 Hyperspectral image visualization using t-distributed stochastic neighbor embedding

Biyin Zhang, Xin Yu

Proceedings Volume 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 981504 (2015) https://doi.org/10.1117/12.2205840
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

Hyperspectral image visualization reduces high-dimensional spectral bands to three color channels, which are sought in order to explain well the nonlinear data characteristics that are hidden in the high-dimensional spectral bands. Despite the surge in the linear visualization techniques, the development of nonlinear visualization has been limited. The paper presents a new technique for visualization of hyperspectral image using t-distributed stochastic neighbor embedding, called VHI-tSNE, which learns a nonlinear mapping between the high-dimensional spectral space and the three-dimensional color space. VHI-tSNE transforms hyperspectral data into bilateral probability similarities, and employs a heavy-tailed distribution in three-dimensional color space to alleviate the crowding problem and optimization problem in SNE technique. We evaluate the performance of VHI-tSNE in experiments on several hyperspectral imageries, in which we compare it to the performance of other state-of-art techniques. The results of experiments demonstrated the strength of the proposed technique.

Citation Download Citation

Biyin Zhang and Xin Yu "Hyperspectral image visualization using t-distributed stochastic neighbor embedding", Proc. SPIE 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 981504 (14 December 2015); https://doi.org/10.1117/12.2205840

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KEYWORDS

Visualization

Hyperspectral imaging

Image visualization

Visual process modeling

Principal component analysis

Stochastic processes

Data modeling

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