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Hyperspectral image (HSI) with high spectral resolution often suffers from low spatial resolution owing to the limitations of imaging sensors. Image fusion is an effective and economical way to enhance the spatial resolution of HS...
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Hyperspectral image (HSI) with high spectral resolution often suffers from low spatial resolution owing to the limitations of imaging sensors. Image fusion is an effective and economical way to enhance the spatial resolution of HSI, which combines HSI with higher spatial resolution multispectral image (MSI) of the same scenario. In the past years, many HSI and MSI fusion algorithms are introduced to obtain high-resolution HSI. However, it lacks a full-scale review for the newly proposed HSI and MSI fusion approaches. To tackle this problem, this work gives a comprehensive review and new guidelines for HSI?MSI fusion. According to the characteristics of HSI?MSI fusion methods, they are categorized as four categories, including pan-sharpening based approaches, matrix factorization based approaches, tensor representation based approaches, and deep convolution neural network based approaches. We make a detailed introduction, discussions, and comparison for the fusion methods in each category. Additionally, the existing challenges and possible future directions for the HSI?MSI fusion are presented.
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In this paper, we investigate a low-complexity scheme for decoding compressed hyperspectral image data. We have exploited the simplicity of the subgradient method by modifying a total variation-based regularization problem to incl...
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In this paper, we investigate a low-complexity scheme for decoding compressed hyperspectral image data. We have exploited the simplicity of the subgradient method by modifying a total variation-based regularization problem to include a residual constraint, employing convex optimality conditions to provide equivalency between the original and reformed problem statements. A scheme that utilizes spectral smoothness by calculating informed starting points to improve the rate of convergence is introduced. We conduct numerical experiments, using both synthetic and real hyperspectral data, to demonstrate the effectiveness of the reconstruction algorithm and the validity of our method for exploiting spectral smoothness. Evidence from these experiments suggests that the proposed methods have the potential to improve the quality and run times of the future compressed hyperspectral image reconstructions.
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To overcome the problems of imaging speed and bulky volume of the traditional hyperspectral imaging systems, the recently proposed compact, snapshot hyperspectral imaging system with diffracted rotation has attracted a lot of inte...
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To overcome the problems of imaging speed and bulky volume of the traditional hyperspectral imaging systems, the recently proposed compact, snapshot hyperspectral imaging system with diffracted rotation has attracted a lot of interest. Due to the severe degradation of the diffracted rotation blurred image, the restored hyperspectral image (HSI) suffers from a lack of spatial detail information and spectral accuracy. To improve the quality of the reconstructed HSI, we present a joint imaging system of diffractive imaging and clear imaging as well as a convolutional neural network (CNN) based method with two input branches for HSI reconstruction. In the recon-struction network, we develop a feature extraction block (FEB) to extract the features of the two input images, respectively. Subsequently, a double residual block (DRB) is designed to fuse and reconstruct the extracted fea-tures. Experimental results show that HSI with high spatial resolution and spectral accuracy can be reconstructed. Our method outperforms the state-of-the-art methods in terms of quantitative metrics and visual quality.
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? 2022 Elsevier B.V.Hyperspectral imaging is the basis for many data analysis techniques. As more application scenarios demand the portability and snapshot imaging capabilities of hyperspectral imaging systems, snapshot spectral i...
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? 2022 Elsevier B.V.Hyperspectral imaging is the basis for many data analysis techniques. As more application scenarios demand the portability and snapshot imaging capabilities of hyperspectral imaging systems, snapshot spectral imaging system based on diffracted rotation has gained increasing attention and development. However, in this system, the traditional iterative optimization-based unrolled network architectures require the assistance of point spread functions (PSFs) in the reconstruction process and are costly in terms of time and computational resources. Aiming to improve the quality of the reconstruction results, reduce the consumption of computational resources and time, and broaden the spectral range, we firstly construct a visible to near-infrared hyperspectral (VNH) dataset. Then we propose a convolutional neural network (CNN) based blind restoration network for hyperspectral images. In this work, we take Unet as the initial framework and propose using the spectral upsampling block, the weight adaptive residual (WAR) block, and the hybrid loss function to enhance the network performance. Experiments show that our proposed method can effectively reconstruct hyperspectral images from rotational diffraction blurred images, consuming less computational resources while retaining a competitive spatial resolution and spectral precision.
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Over the past 20 years, hyperspectral microscopy has grown into a robust field of analysis for a number of applications. The visible to near-infrared (VNIR; 400 to 1000 nm) region of the spectrum has demonstrated utility for the c...
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Over the past 20 years, hyperspectral microscopy has grown into a robust field of analysis for a number of applications. The visible to near-infrared (VNIR; 400 to 1000 nm) region of the spectrum has demonstrated utility for the characterization of healthy and diseased tissue and of biomolecular indicators at the cellular level. Here, we describe the development of a hyperspectral imaging (HSI) microscope that is aimed at material characterization to complement traditional stand-off, earth remote sensing with hyperspectral sensors. We combine commercial off the shelf technology to build an HSI microscope to collect spectral data with illumination provided by a tunable laser. Hyperspectral imaging microscopy (HIM) facilitates detailed examination of target materials at the subcentimeter spatial scale. The custom-built, laser illumination HSI microscope covers the NIR to shortwave infrared (NIR/SWIR; 900 to 2500 nm) solar-reflected spectral range. It is combined with a separate VNIR sensor (400 to 900 nm) that utilizes quartz-tungsten-halogen lamps for illumination. The combined sensors provide a means to collect >10,000 s of spectra in the full VNIR/SWIR spectral range from both pure substances and precisely engineered linear and nonlinear mixtures. The large abundance of spectra allows for a more detailed understanding of the variability and multivariate probability distributions of spectral signatures. This additional information aids in understanding the variability observed in ground truth spectra collected from portable spectrometers, and it greatly enhances sample description and metadata content. In addition, HIM data cubes can serve as proxies, as "microscenes," for systems engineering applications such as trade studies for HSI acquired by air- and space-borne sensors. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
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Image intensity value is determined by both the albedo component and the shading component. The albedo component describes the physical nature of different objects at the surface of the earth, and land-cover classes are different ...
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Image intensity value is determined by both the albedo component and the shading component. The albedo component describes the physical nature of different objects at the surface of the earth, and land-cover classes are different from each other because of their intrinsic physical materials. We, therefore, recover the intrinsic albedo feature of the hyperspectral image to exploit the spatial semantic information. Then, we use the support vector machine (SVM) to classify the recovered intrinsic albedo hyperspectral image. The SVM tries to maximize the minimum margin to achieve good generalization performance. Experimental results show that the SVM with the intrinsic albedo feature method achieves a better classification performance than the state-of-the-art methods in terms of visual quality and three quantitative metrics. (C) 2017 SPIE and IS&T
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In the search of fast, reliable and cost-efficient methods for industrial mineral sorting, new methods are being investigated. We present a yet untried method, namely the analysis of mineral fluorescence by means of hyperspectral ...
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In the search of fast, reliable and cost-efficient methods for industrial mineral sorting, new methods are being investigated. We present a yet untried method, namely the analysis of mineral fluorescence by means of hyperspectral imaging. For this method, minerals are excited by ultraviolet radiation and their space-resolved visible fluorescence light is recorded spectrally. We present the hyperspectral images as well as improved classification results obtained by the usage of denoised images.
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Rather than simply acting as a photographic camera capturing two-dimensional (x, y) intensity images or a spectrometer acquiring spectra (lambda), a hyperspectral imager measures entire three-dimensional (x, y, lambda) datacubes f...
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Rather than simply acting as a photographic camera capturing two-dimensional (x, y) intensity images or a spectrometer acquiring spectra (lambda), a hyperspectral imager measures entire three-dimensional (x, y, lambda) datacubes for multivariate analysis, providing structural, molecular, and functional information about biological cells or tissue with unprecedented detail. Such data also gives clinical insights for disease diagnosis and treatment. We summarize the principles underpinning this technology, highlight its practical implementation, and discuss its recent applications at microscopic to macroscopic scales.
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Many long-wave infrared spectroscopic imaging applications are limited by the portability and cost of detector arrays. We present a characterization of a newly available, low-cost, uncooled vanadium oxide microbolometer array, the...
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Many long-wave infrared spectroscopic imaging applications are limited by the portability and cost of detector arrays. We present a characterization of a newly available, low-cost, uncooled vanadium oxide microbolometer array, the Seek Compact, in accordance with common infrared detector specifications: noise-equivalent differential temperature (NEDT), optical responsivity spectra, and Allan variance. The Compact's imaging array consists of 156x206 pixels with a 12-μm pixel pitch, 93% of the pixels yield useful temperature readings. Characterization results show optical response between X = 7.4 and 12 μm with an NEDT of 148 mK (at ≈7 fps). Comparing these results to a research-grade camera, the Seek Compact exhibits a 4x and 48x reduction in weight (2.0/0.5 lbs) and cost ($12,000/$250) but takes 93x longer to achieve the same NEDT (1.55 s/16.6 ms for 45 mK). Additionally, a proof-of-concept spectral imaging experiment of SiN thin films is conducted. Leveraging this price reduction and spectroscopic imaging capability, the Seek Compact has potential in enabling field-deployable and distributed active midinfrared spectroscopic imaging, where cost and portability are the dominate inhibitors and high frame rates are not required. «
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