Local detail enhanced hyperspectral image visualization

Fang, Jialu; Qian, Yuntao

doi:10.1109/igarss.2015.7325960

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…A set of hyperspectral image visualization approaches are based on digital image processing techniques. In [20], dimension reduction is achieved using multidimensional scaling, followed by detail enhancement using a Laplacian pyramid. 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.…”

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Feature Extraction Techniques for Hyperspectral Images Classification

Fejjari

Ettabaa

Korbaa

2020

Advances in Intelligent Systems and Computing

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A Multi-Spectral Fractal Image Model and Its Associated Fractal Dimension Estimator

Ivanovici

2023

Fractal Fract

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We propose both a probabilistic fractal model and fractal dimension estimator for multi-spectral images. The model is based on the widely known fractional Brownian motion fractal model, which is extended to the case of images with multiple spectral bands. The model is validated mathematically under the assumption of statistical independence of the spectral components. Using this model, we generate several synthetic multi-spectral fractal images of varying complexity, with seven statistically independent spectral bands at specific wavelengths in the visible domain. The fractal dimension estimator is based on the widely used probabilistic box-counting classical approach extended to the multivariate domain of multi-spectral images. We validate the estimator on the previously generated synthetic multi-spectral images having fractal properties. Furthermore, we deploy the proposed multi-spectral fractal image estimator for the complexity assessment of real remotely sensed data sets and show the usefulness of the proposed approach.

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Local detail enhanced hyperspectral image visualization

Cited by 4 publications

References 7 publications

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

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

Feature Extraction Techniques for Hyperspectral Images Classification

A Multi-Spectral Fractal Image Model and Its Associated Fractal Dimension Estimator

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