Decolorization-Based Hyperspectral Image Visualization

Kang, Xudong; Li, Shutao; Benediktsson, Jón Atli

doi:10.1109/tgrs.2018.2815588

Cited by 47 publications

(16 citation statements)

References 57 publications

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“…Figures 9-13 depict the visualization results for the five hyperspectral test images presented in Section 2. Each figure is organized as follows: on the top row, the results obtained with the proposed linear approach using the Gaussian functions ( Figure 5); on the middle row, the results obtained with the linear approach using camera spectral sensitivity functions ( Figure 4); on the bottom row, the results obtained using the proposed ANN approach (Section 2.4), the approach based on the PCA to RGB mapping [15], the linear approach based on the stretched color matching functions (CMF) [18] and two recent approaches, constrained manifold learning (CML) [25] and decolorization-based hyperspectral visualization (DHV) [21].…”

Section: Resultsmentioning

confidence: 99%

“…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. The technique described in [22] is based on t-distributed stochastic neighbor embedding (t-SNE) and bilateral filtering.…”

Section: Introductionmentioning

confidence: 99%

“…The amount of information in a hyperspectral image should be preserved as much as possible after the visualization. The entropy is often used to measure the amount of information contained by a signal [30] and is one of the metrics that are used for the objective assessment of the visualization result [10,21,31]. The complexity of a scene is related to the texture and object characteristics preservation in the process of visualization.…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…4 different decolorization algorithms are used to generate panchromatic image, which are 1, Reflectance Mean of all bands; 2, Radiance integration of all bands; 3, FakeGray: weighted sum of fake RGB bands (640nm, 550nm and 470nm); 4. Contrast Preserved Decolorization algorithm proposed by Kang et al 2018.…”

Section: Stacked Keypoint Detectionmentioning

confidence: 99%

Stacked Local Feature Detector for Hyperspectral Image

Yan

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Images registration is an important task in hyperspectral image processing, while almost all local feature point based image matching algorithm is designed for single band image only and miss the advantage of abundant spectral information. Therefor, in this paper we propose a novel local feature detector for hyperspectral image. This method, which is named stacked local feature detector (HSI-SFD), stack all local feature points detected from every single spectral band. With redundant information, local feature miss-detected at noise pixel can be filtered out by the stack count threshold, leading to more reliable and robust local features. To verify the algorithm, a hyperspectral image matching dataset is built. Multiview hyperspectral images of the several different flat targets are taken by scan-line hyperspectral camera in library. Each image contains 270 bands within 400–1000 nm. Groundtruth transformation matrix between images is computed from corresponding points selected by hands. Experiment on the dataset shows that features points miss-detected at texture-less area can be inhibited by HSI-SFD. Both keypoint repeatability and matching accuracy increase significantly. Precision and matching score increase up to 10% for some scene.

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“…n the last 1-2 decades, hyperspectral images (HSIs) have been widely and successfully applied in many application fields, such as crop analysis, geological research, environment mapping and the geology [1][2][3][4]. A pixel in HSIs is a high-dimensional vector which contains the spectral responses from various spectral bands.…”

Section: Introductionmentioning

confidence: 99%