Hyperspectral image visualization using t-distributed stochastic neighbor embedding

Zhang, Biyin; Yu, Xin

doi:10.1117/12.2205840

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…In remote sensing where reference data are typically sparse, many approaches have been explored. For example, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (tSNE) are dimensionality reduction methods commonly used to reduce HSI to two or three dimensions for visualization [13,14]. Similarly for endmember extraction there are a variety of established approaches including geometric methods like vertex component analysis, statistical methods like k-nearest neighbors and non-negative matrix factorization (NMF), and deep learning methods based on autoencoder architectures [15][16][17][18][19][20][21].…”

Section: Of 17mentioning

confidence: 99%

Unsupervised Characterization of Water Composition with UAV-based Hyperspectral Imaging and Generative Topographic Mapping

Waczak,

Aker,

Wijeratne

et al. 2024

Preprint

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Unmanned Aerial Vehicles (UAVs) equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of a plethora of optically-active water quality parameters via band ratio algorithms and machine learning methods. However, fitting and validating these models requires access to sufficient quantities of in situ reference data which are time-consuming and expensive to obtain. In this study, we demonstrate how the Generative Topographic Mapping (GTM), a Bayesian realization of the Self-organizing Map, can be used to visualize high-dimensional hyperspectral imagery and extract spectral signatures corresponding to unique endmembers present in the water. Using data collected across a North Texas pond, we first apply the GTM to visualize the distribution of captured reflectance spectra revealing small-scale spatial variability of water composition. Next, we demonstrate how the nodes of the fitted GTM can be interpreted as unique spectral endmembers. Using extracted endmembers together with the normalized spectral similarity score, we are able to efficiently map the abundance of near shore algae as well as the evolution of a rhodamine tracer dye used to simulate water contamination by a localized source.

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Section: Of 17mentioning

confidence: 99%

Unsupervised Characterization of Water Composition with UAV-based Hyperspectral Imaging and Generative Topographic Mapping

Waczak,

Aker,

Wijeratne

et al. 2024

Preprint

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“…The latter allows for taking into account, among other things, the spatial location of the pixels. For example, t-SNE [14], UMAP [5], and manifold alignment [15], etc. [16,17] are used as such techniques.…”

Section: Introductionmentioning

confidence: 99%

wEscore: quality assessment method of multichannel image visualization with regard to angular resolution

Sidorchuk

2022

Computer Optics

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This work considers the problem of quality assessment of multichannel image visualization methods. One approach to such an assessment, the Escore quality measure, is studied. This measure, initially proposed for decolorization methods evaluation, can be generalized for the assessment of hyperspectral image visualization methods. It is shown that Escore does not account for the loss of local contrast at the supra-pixel scale. The sensitivity to the latter in humans depends on the observation conditions, so we propose a modified wEscore measure which includes the parameters allowing for the adjustment of the local contrast scale based on the angular resolution of the images. We also describe the adjustment of wEscore parameters for the evaluation of known decolorization algorithms applied to the images from the COLOR250 and the Cadik datasets with given observational conditions. When ranking the results of these algorithms and comparing it to the ranking based on human perception, wEscore turned out to be more accurate than Escore.

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“…The approach presented in [21] uses the averaging method in order to the number of bands to 9; a decolorization algorithm is then applied on groups of three adjacent channels, which produces the final color image. The technique described in [22] is based on t-distributed stochastic neighbor embedding (t-SNE) and bilateral filtering. The method in [23] is also based on bilateral filtering, together with high dynamic range (HDR) processing techniques, while in [24] a pairwise-distances-analysis-driven visualization technique is described.…”

Section: Introductionmentioning

confidence: 99%

Linear and Non-Linear Models for Remotely-Sensed Hyperspectral Image Visualization

et al. 2020

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The visualization of hyperspectral images still constitutes an open question and may have an important impact on the consequent analysis tasks. The existing techniques fall mainly in the following categories: band selection, PCA-based approaches, linear approaches, approaches based on digital image processing techniques and machine/deep learning methods. In this article, we propose the usage of a linear model for color formation, to emulate the image acquisition process by a digital color camera. We show how the choice of spectral sensitivity curves has an impact on the visualization of hyperspectral images as RGB color images. In addition, we propose a non-linear model based on an artificial neural network. We objectively assess the impact and the intrinsic quality of the hyperspectral image visualization from the point of view of the amount of information and complexity: (i) in order to objectively quantify the amount of information present in the image, we use the color entropy as a metric; (ii) for the evaluation of the complexity of the scene we employ the color fractal dimension, as an indication of detail and texture characteristics of the image. For comparison, we use several state-of-the-art visualization techniques. We present experimental results on visualization using both the linear and non-linear color formation models, in comparison with four other methods and report on the superiority of the proposed non-linear model.

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Hyperspectral image visualization using t-distributed stochastic neighbor embedding

Cited by 5 publications

References 8 publications

Unsupervised Characterization of Water Composition with UAV-based Hyperspectral Imaging and Generative Topographic Mapping

Unsupervised Characterization of Water Composition with UAV-based Hyperspectral Imaging and Generative Topographic Mapping

wEscore: quality assessment method of multichannel image visualization with regard to angular resolution

Linear and Non-Linear Models for Remotely-Sensed Hyperspectral Image Visualization

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