FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding

Najim, Safa A.; Lim, Ik Soo; Wittek, Peter; Jones, Mark W.

doi:10.1109/lgrs.2014.2324631

Cited by 13 publications

(4 citation statements)

References 20 publications

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“…A functional description of this process can deepen understanding of the steps comprising the visualization process [12][13][14][15]. The visualization process in Equation ( 1) can be visualized as a function Vis that maps between the set of all possible types of raw input data, known as D I , and the set of created images, known as V [16]:…”

Section: Objectives Of This Workmentioning

confidence: 99%

Visualizing and Evaluating Microbubbles in Multiphase Flow Applications

Najim,

Meerakaviyad,

Pun

et al. 2024

Fluids

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Accurate visualization of bubbles in multiphase flow is a crucial aspect of modeling heat transfer, mixing, and turbulence processes. It has many applications, including chemical processes, wastewater treatment, and aquaculture. A new software, Flow_Vis, based on experimental data visualization, has been developed to visualize the movement and size distribution of bubbles within multiphase flow. Images and videos recorded from an experimental rig designed to generate microbubbles were analyzed using the new software. The bubbles in the fluid were examined and found to move with different velocities due to their varying sizes. The software was used to measure bubble size distributions, and the obtained results were compared with experimental measurements, showing reasonable accuracy. The velocity measurements were also compared with literature values and found to be equally accurate.

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Section: Objectives Of This Workmentioning

confidence: 99%

Visualizing and Evaluating Microbubbles in Multiphase Flow Applications

Najim,

Meerakaviyad,

Pun

et al. 2024

Fluids

Self Cite

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“…Thus, data visualization is one of the best solutions to find best result with ability in preserving the information [8]. The conversion of high-dimensional data to a lower dimension while keeping the key properties of the original space is a common requirement in visualization [9].…”

Section: Data Visualizationmentioning

confidence: 99%

A Comparative Study for Skeleton Representation Methods Using Data Visualization

Zalaan,

Najimb

2022

J. Al-Qadisiyah Comp. Sci. Math.

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Data visualization is a technique used to see the unseen information, that will help humanity to discover important things. There are many methods to represent the datasets, but data visualization is the best because it can preserve the information. Skeleton is used to analyse the visualization, thus data visualization gives efficient results. Comparison among well-known methods is the goal of this paper. The conclusion of this paper showed all the comparative results will be important for any further study. This study will use data visualization to discover novel dataset representations, which is the main goal of data visualization and can be useful in presenting necessary data.

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“…To model the nonlinear data structure in HSIs [9], manifold learning methods have been proposed to represent the topology of high dimensional HSIs in lower dimensions for visualization and dimension reduction. Isometric feature mapping (ISOMAP) [9], [10], kernel principal component analysis (KPCA) [11], Laplacian Eigenmaps [12], Locality Preserving Projection (LPP) [13] and locally linear embedding (LLE) [14], [15] have received much attention because of their firm theoretical foundation associated with the kernel and eigenspectrum framework.…”

Section: Introductionmentioning

confidence: 99%

Constrained Manifold Learning for Hyperspectral Imagery Visualization

Liao

Qian

Tang

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Displaying the large number of bands in a hyperspectral image (HSI) on a trichromatic monitor is important for HSI processing and analysis system. The visualized image shall convey as much information as possible from the original HSI and meanwhile facilitate image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a visualization approach based on constrained manifold learning, whose goal is to learn a visualized image that not only preserves the manifold structure of the HSI but also has natural colors. Manifold learning preserves the image structure by forcing pixels with similar signatures to be displayed with similar colors. A composite kernel is applied in manifold learning to incorporate both the spatial and spectral information of HSI in the embedded space. The colors of the output image are constrained by a corresponding natural-looking RGB image, which can either be generated from the HSI itself (e.g., band selection from the visible wavelength) or be captured by a separate device. Our method can be done at instance-level and feature-level. Instance-level learning directly obtains the RGB coordinates for the pixels in the HSI while feature-level learning learns an explicit mapping function from the high dimensional spectral space to the RGB space. Experimental results demonstrate the advantage of the proposed method in information preservation and natural color visualization.

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FSPE: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding

Cited by 13 publications

References 20 publications

Visualizing and Evaluating Microbubbles in Multiphase Flow Applications

Visualizing and Evaluating Microbubbles in Multiphase Flow Applications

A Comparative Study for Skeleton Representation Methods Using Data Visualization

Constrained Manifold Learning for Hyperspectral Imagery Visualization

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