KEYWORDS: Dimension reduction, Visualization, Hyperspectral imaging, Image visualization, Visual process modeling, Stochastic processes, Principal component analysis, Data modeling, Optimization (mathematics), RGB color model
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.
This paper proposes a novel multiscale graph cut based analysis framework for the supervised classification of
hyperspectral imagery. This framework is aimed at obtaining accurate and reliable maps by properly considering the
spatial-context information. It is made up of two main blocks: 1) a feature-extraction block exploits an object-oriented
analysis and representation of hyperspectral imagery that is obtained by multiscale graph cut (MGC) based segmentation;
2) a classifier, based on support vector machines (SVMs), capable of analyzing hyperdimensional feature spaces.
Experimental results confirm the effectiveness of the proposed system for the analysis of hyperspectral imagery.
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