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hyperspectral image visualization with edge-preserving filtering and principal component analysis

INFORMATION FUSION 2020年 57卷 130-143页

作者： Kang, Xudong Duan, Puhong Li, Shutao Hunan Univ Coll Elect & Engn Changsha Peoples R China Key Lab Visual Percept & Artificial Intelligence Changsha Peoples R China

In this paper, an edge-preserving filtering and principal component analysis (PCA)-based visualization method is proposed for hyperspectral images, in which both global and local image information of hyperspectral images (HSIs) are taken into account in the proposed visualization framework that consists of the following major steps. First, the band number of the original image is reduced with averaging-based image fusion (AIF). Then, the edge-preserving filtering is performed on the dimension reduced image so as to decompose it into two components, i.e., the base layers which contain the large-scale boundary information, and the detail layers which contain the mid-and small-scale edges and textures. Next, the base layers are fused with the principal component analysis method, and the detail layers are fused with the weighted sum method, in which the fusion weights are determined by the transform matrix of the base layers. Finally, the fused detail layers are enhanced with histogram equalization and combined with the fused base layers to visualize the hyperspectral image. Experimental results on four real hyperspectral data sets demonstrate that the proposed approach performs the best in improving image contrast and preserving the details. In addition, compared with seven state-of-the-art visualization methods, the quantitative metrics obtained by the proposed method on four data sets have been increased by 80.45%, 69.29%, 138.61%, and 23.21% on average in terms of separability of features (SF), standard deviation (SD), average gradient (AG), and entropy (EN).

关键词： hyperspectral image visualization Principal component analysis Edge-preserving filtering Dimension reduction

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Pairwise-Distance-Analysis-Driven Dimensionality Reduction Model with Double Mappings for hyperspectral image visualization

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REMOTE SENSING 2015年第6期7卷 7785-7808页

作者： Long, Yi Li, Heng-Chao Celik, Turgay Longbotham, Nathan Emery, William J. Southwest Jiaotong Univ Sichuan Prov Key Lab Informat Coding & Transmiss Chengdu 610031 Peoples R China Guizhou Univ Coll Big Data & Informat Engn Sci Guiyang 550025 Peoples R China Univ Witwatersrand Sch Comp Sci ZA-2000 Johannesburg South Africa DigitalGlobe Inc Longmont CO 80503 USA Univ Colorado Dept Aerosp Engn Sci Boulder CO 80309 USA

This paper describes a novel strategy for the visualization of hyperspectral imagery based on the analysis of image pixel pairwise distances. The goal of this approach is to generate a final color image with excellent interpretability and high contrast at the cost of distorting a few pairwise distances. Specifically, the principle of equal variance is introduced to divide all hyperspectral bands into three subgroups and to ensure the energy is distributed uniformly between them, as in natural color images. Then, after detecting both normal and outlier pixels, these three subgroups are mapped into three color components of the output visualization using two different mapping (i.e., dimensionality reduction) schemes for the two types of pixels. The widely-used multidimensional scaling (MDS) is used for normal pixels and a new objective function, taking into account the weighting of pairwise distances, is presented for the outlier pixels. The pairwise distance weighting is designed such that small pairwise distances between the outliers and their respective neighbors are emphasized and large deviations are suppressed. This produces an image with high contrast and good interpretability while retaining the detailed information content. The proposed algorithm is compared with several state-of-the-art visualization techniques and evaluated on the well-known AVIRIS hyperspectral images. The effectiveness of the proposed strategy is substantiated both visually and quantitatively.

关键词： hyperspectral image visualization dimensionality reduction multidimensional scaling human visual system

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Decolorization-Based hyperspectral image visualization

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2018年第8期56卷 4346-4360页

作者： Kang, Xudong Duan, Puhong Li, Shutao Benediktsson, Jon Atli Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Univ Iceland Fac Elect & Comp Engn IS-101 Reykjavik Iceland

image decolorization is known to be an effective way in transferring a color image into a gray one while well preserving the major information of all three bands. In this paper, a simple yet effective hyperspectral image visualization framework based on decolorization, named decolorization based hyperspectral visualization, is proposed, which enables us to fully exploit the benefits of decolorization technique. The proposed framework consists of the following two main steps. First, the hyperspectral image is partitioned into nine subsets of adjacent hyperspectral bands and the averaged band of each subset is calculated. Then, the dimension reduced image is further divided into three groups of adjacent bands, and the bands in each group are fused by using an image decolorization method. The main contribution of this paper is that the strong correlations in two different fields, i.e., image decolorization and hyperspectral image visualization, are first built. Experiments performed on several real hyperspectral data sets demonstrate that the proposed framework can obtain outstanding visualization performance in terms of both subjective and objective evaluations.

关键词： Averaging-based method band segmentation hyperspectral image visualization image decolorization

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Interactive hyperspectral image visualization Using Convex Optimization

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2009年第6期47卷 1673-1684页

作者： Cui, Ming Razdan, Anshuman Hu, Jiuxiang Wonka, Peter Arizona State Univ Dept Comp Sci & Engn Partnership Res Spatial Modeling PRISM Lab Tempe AZ 85287 USA Arizona State Univ Div Comp Studies I3DEA Lab Mesa AZ 85212 USA

In this paper, we propose a new framework to visualize hyperspectral images. We present three goals for such a visualization: 1) preservation of spectral distances;2) discriminability of pixels with different spectral signatures;3) and interactive visualization for analysis. The introduced method considers all three goals at the same time and produces higher quality output than existing methods. The technical contribution of our mapping is to derive a simplified convex optimization from a complex nonlinear optimization problem. During interactive visualization, we can map the spectral signature of pixels to red, green, and blue colors using a combination of principal component analysis and linear programming. In the results, we present a quantitative analysis to demonstrate the favorable attributes of our algorithm.

关键词： hyperspectral image visualization linear programming perceptual color distances principal component analysis (PCA)

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A MULTIVARIATE GRADIENT AND MUTUAL INFORMATION MEASURE METHOD FOR hyperspectral image visualization 38

A MULTIVARIATE GRADIENT AND MUTUAL INFORMATION MEASURE METHO...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Amankwah, Anthony Amankwah Consult Ltd Garten Str 13 Stuttgart Germany

ISBN: (纸本)9781538671504

hyperspectral imaging is becoming relevant in many applications. A large number of bands can be used to distinguish between different materials, quality inspection and mineral exploration. However, the information gathered by the narrow spectral bands in a hyperspectral image is not only large, but shows a high degree of correlation between bands. This redundancy needs to be reduced for efficient computation and storage. In this paper, we propose a new similarity metric, which combines gradient information and mutual information as positive interactive information. We apply the similarity metric for the visualization of hyperspectral images. Experimental methods show that the proposed method is superior to the state-of-the-art methods.

关键词： Multivariate mutual information hyperspectral image visualization

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Maximum Distance-based PCA Approximation for hyperspectral image Analysis and visualization 41

Maximum Distance-based PCA Approximation for Hyperspectral I...

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41st International Conference on Telecommunications and Signal Processing (TSP)

作者： Machidon, Alina Coliban, Radu Machidon, Octavian Ivanovici, Mihai Transilvania Univ Brasov Dept Elect & Comp Brasov Romania

ISBN: (纸本)9781538646953

Principal Component Analysis (PCA) is a widely-used statistical tool for multivariate signal analysis, including optimized representation, compression and visualization in the case of hyperspectral satellite images. We propose a geometrical construction of an approximation of the PCA based on the maximum distance instead of the classical statistical approach based on computing the eigenvectors of the data covariance matrix. We validate our approach on synthetic 2D data with Gaussian distribution and prove its usefulness for visualization of hyperspectral satellite images. We discuss our results and draw the conclusions.

关键词： hyperspectral image visualization PCA approximation satellite image analysis

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Remote Sensing image visualization Using Double Layers

Remote Sensing Image Visualization Using Double Layers

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Cai, Shangshu Du, Qian Moorhead, Robert Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA

ISBN: (纸本)9780780395091

Remote sensing imagery has relatively low spatial resolution. Since different materials/objects may be present in the area covered by a pixel, the pixel reflectance can be considered as the linear mixture of reflectance of these materials/objects (i.e., endmembers). Thus in remote sensing image processing we deal with mixed pixels instead of pure pixels as in traditional digital image processing. A multi-spectral or hyper-spectral image has tens to hundreds of spectral bands. How to display the plentiful information contained in such a 3D image cube is a challenging problem. In this paper, we will propose a visualization technique that employs two layers to integrate the mixture information (i.e., endmembers;and their functional abundances) in each pixel. With user-friendly interactive tools, images can be displayed at any desired level of details.

关键词： hyperspectral image visualization unsupervised classification Linear unmixing

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

COMPUTER OPTICS

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COMPUTER OPTICS 2022年第1期46卷 113-120页

作者： Sidorchuk, D. S. Russian Acad Sci Inst Informat Transmiss Problems Kharkevich Inst Bolshoy Karetny Pereulok 19 Moscow 127051 Russia

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.

关键词： hyperspectral image visualization decolorization Escore local contrast

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Feature-Driven Multilayer visualization for Remotely Sensed hyperspectral imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2010年第9期48卷 3471-3481页

作者： Cai, Shangshu Du, Qian Moorhead, Robert J. Univ Calif Santa Barbara Ctr Risk Studies & Safety Goleta CA 93117 USA Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA Mississippi State Univ High Performance Comp Collaboratory Geosyst Res Inst Mississippi State MS 39762 USA

Displaying the abundant information contained in a remotely sensed hyperspectral image is a challenging problem. Currently, no approach can satisfactorily render the desired information at arbitrary levels of detail. In this paper, we present a feature-driven multilayer visualization technique that automatically chooses data visualization techniques based on the spatial distribution and importance of the endmembers. It can simultaneously visualize the overall material distribution, subpixel level details, and target pixels and materials. By incorporating interactive tools, different levels of detail can be presented per users' request. This scheme employs five layers from the bottom to the top: the background layer, data-driven spot layer, pie-chart layer, oriented sliver layer, and anomaly layer. The background layer provides the basic tone of the display;the data-driven spot layer manifests the overall material distribution in an image scene;the pie-chart layer presents the precise abundances of endmember materials in each pixel;the oriented sliver layer emphasizes the distribution of important anomalous materials;and the anomaly layer highlights anomaly pixels (i.e., potential targets). Displays of the airborne AVIRIS data and spaceborne Hyperion data demonstrate that the proposed multilayer visualization scheme can efficiently display more information globally and locally.

关键词： hyperspectral image visualization mixed-pixel visualization multilayer visualization

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CONVOLUTIONAL NEURAL NETWORK FOR NATURAL COLOR visualization OF hyperspectral imageS 39

CONVOLUTIONAL NEURAL NETWORK FOR NATURAL COLOR VISUALIZATION...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Duan, Puhong Kang, Xudong Li, Shutao Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)9781538691540

In this paper, a novel deep learning based visualization method is proposed for natural color visualization of hyperspectral images, which consists of the following steps. First, the spectral bands of the hyperspectral image are divided into two groups, i.e., the red, green, and blue (RGB) bands and the remaining bands. Then, a pretrained convolutional neural network (CNN) model, i.e., VGG-19, is explored to fuse the remaining bands so as to obtain a fused band with rich details. Next, the intensity-hue-saturation (IHS) transform is performed on the averaged red, green, and blue bands to obtain three different components, i.e., intensity (I), hue (H), saturation (S). Finally, the fused band is utilized to replace the intensity component followed by an inverse IHS transform. Experiments performed on two hyperspectral data sets demonstrate that the proposed method cannot only obtain a natural color resulting image, but also well preserving image details with respect to several state-of-the-art methods.

关键词： Convolutional neural network band selection hyperspectral image visualization

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