Search Results (12)

HyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular design, is compact and lightweight, and low-cost using commercial off-the-shelf (COTS) optical components. The modular design allows for flexible and rapid development, as well as validation within college lab environments. To optimize space utilization and reduce the optical path, HyperSCAN’s optical system incorporates a folding mirror, making it ideal for the constrained environment of a CubeSat. The use of COTS components significantly lowers pre-development costs and minimizes associated risks. The compact size and cost-effectiveness of CubeSats, combined with the advanced capabilities of hyperspectral imagers, make them a powerful tool for a broad range of applications, such as environmental monitoring of Earth, disaster management, mineral and resource exploration, atmospheric and climate studies, and coastal and marine research. We conducted a spatial-resolution-boost experiment using HyperSCAN data and various hyperspectral datasets including Urban, Pavia University, Pavia Centre, Botswana, and Indian Pines. After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. With the aid of higher-spatial-resolution multispectral data, we can boost the spatial resolution of HyperSCAN data. Full article

Spectral imaging technology, which aims to capture images across multiple spectral channels and create a spectral data cube, has been widely utilized in various fields. However, conventional spectral imaging systems face challenges, such as slow acquisition speed and large size. The rapid development of optical metasurfaces, capable of manipulating light fields versatilely and miniaturizing optical components into ultrathin planar devices, offers a promising solution for compact hyperspectral imaging (HSI). This study proposes a compact snapshot compressive spectral imaging (SCSI) system by leveraging the spectral modulations of metasurfaces with dual-channel switchable metasurface filters and employing a deep-learning-based reconstruction algorithm. To achieve compactness, the proposed system integrates dual-channel switchable metasurface filters using twisted nematic liquid crystals (TNLCs) and anisotropic titanium dioxide (TiO₂) nanostructures. These thin metasurface filters are closely attached to the image sensor, resulting in a compact system. The TNLCs possess a broadband linear polarization conversion ability, enabling the rapid switching of the incidence polarization state between x-polarization and y-polarization by applying different voltages. This polarization conversion facilitates the generation of two groups of transmittance spectra for wavelength-encoding, providing richer information for spectral data cube reconstruction compared to that of other snapshot compressive spectral imaging techniques. In addition, instead of employing classic iterative compressive sensing (CS) algorithms, an end-to-end residual neural network (ResNet) is utilized to reconstruct the spectral data cube. This neural network leverages the 2-frame snapshot measurements of orthogonal polarization channels. The proposed hyperspectral imaging technology demonstrates superior reconstruction quality and speed compared to those of the traditional compressive hyperspectral image recovery methods. As a result, it is expected that this technology will have substantial implications in various domains, including but not limited to object detection, face recognition, food safety, biomedical imaging, agriculture surveillance, and so on. Full article

In order to capture the spatial-spectral $(x,y,\lambda )$ information of the scene, various techniques have been proposed. Different from the widely used scanning-based methods, spectral snapshot compressive imaging (SCI) utilizes the idea of compressive sensing to compressively capture the 3D spatial-spectral data-cube in a single-shot 2D measurement and thus it is efficient, enjoying the advantages of high-speed and low bandwidth. However, the reconstruction process, i.e., to retrieve the 3D cube from the 2D measurement, is an ill-posed problem and it is challenging to reconstruct high quality images. Previous works usually use 2D convolutions and preliminary attention to address this challenge. However, these networks and attention do not exactly extract spectral features. On the other hand, 3D convolutions can extract more features in a 3D cube, but increase computational cost significantly. To balance this trade-off, in this paper, we propose a hybrid multi-dimensional attention U-Net (HMDAU-Net) to reconstruct hyperspectral images from the 2D measurement in an end-to-end manner. HMDAU-Net integrates 3D and 2D convolutions in an encoder–decoder structure to fully utilize the abundant spectral information of hyperspectral images with a trade-off between performance and computational cost. Furthermore, attention gates are employed to highlight salient features and suppress the noise carried by the skip connections. Our proposed HMDAU-Net achieves superior performance over previous state-of-the-art reconstruction algorithms. Full article

The design and calibration of the dispersive device in a hyperspectral imager significantly affect the performance of hyperspectral imaging, especially the spectral accuracy. To achieve high-accuracy hyperspectral imaging over the visible band, firstly, the geometric and dispersive parameters of the double Amici prism (DAP) that serves as a dispersive device in the direct-vision push-broom compressive hyperspectral imager (PBCHI) are designed and optimized; secondly, a calibration method based on the numerical calculation of the DAP model is put forward, which can turn the conventional pixel-wise dispersive shift calibration by a monochromator into a group of numerical calculations; lastly, a PBCHI prototype is built to test the performances of the designed and calibrated DAP and the hyperspectral imager. The calibration experiments demonstrate that the mean squared error (MSE) of the dispersive pixel shifts calibrated by the proposed numerical method is 0.1774, which indicates the calibration result of the proposed method is consistent with the directly calibrated result. Furthermore, after this numerical calculation, the spectral signatures of the reconstructed cubes of the DAP-based PBCHI system show consistency with the ground truth. This work will benefit the design and calibration of the DAP-based hyperspectral imager. Full article

Hyperspectral images (HSIs) are data cubes containing rich spectral information, making them beneficial to many Earth observation missions. However, due to the limitations of the associated imaging systems and their sensors, such as the swath width and revisit period, hyperspectral imagery over a large coverage area cannot be acquired in a short amount of time. Spectral super-resolution (SSR) is a method that involves learning the relationship between a multispectral image (MSI) and an HSI, based on the overlap region, followed by reconstruction of the HSI by making full use of the large swath width of the MSI, thereby improving its coverage. Much research has been conducted recently to address this issue, but most existing methods mainly learn the prior spectral information from training data, lacking constraints on the resulting spectral fidelity. To address this problem, a novel learning spectral transformer network (LSTNet) is proposed in this paper, utilizing a reference-based learning strategy to transfer the spectral structure knowledge of a reference HSI to create a reasonable reconstruction spectrum. More specifically, a spectral transformer module (STM) and a spectral reconstruction module (SRM) are designed, in order to exploit the prior and reference spectral information. Experimental results demonstrate that the proposed method has the ability to produce high-fidelity reconstructed spectra. Full article

We propose a real-time hyperspectral video acquisition system that uses coded slits. Conventional imaging spectrometers usually have scanning mechanisms that reduce the temporal resolution or sacrifice the spatial resolution to acquire spectral information instantly. Recently, computational spectral imaging has been applied to realize high-speed or high-performance spectral imaging. However, the most current computational spectral imaging systems take a long time to reconstruct spectral data cubes from limited measurements, which limits real-time hyperspectral video acquisition. In this work, we propose a new computational spectral imaging system. We substitute the slit in a conventional scanning-based imaging spectrometer with coded slits, which can achieve the parallel acquisition of spectral data and thus an imaging speed that is several times higher. We also apply an electronically controlled translation stage to use different codes at each exposure level. The larger amount of data allows for fast reconstruction through matrix inversion. To solve the problem of a trade-off between imaging speed and image quality in high-speed spectral imaging, we analyze the noise in the system. The severe readout noise in our system is suppressed with S-matrix coding. Finally, we build a practical prototype that can acquire hyperspectral video with a high spatial resolution and a high signal-to-noise ratio at 5 Hz in real time. Full article

Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397–1000 nm range). The proposed method is based on the visualization of heme-components bands in the 500–700 nm spectral range. For experimental and validation purposes, a total of 225 blood (different donors) and non-blood (protein-based ketchup, rust acrylic paint, red acrylic paint, brown acrylic paint, red nail polish, rust nail polish, fake blood, and red ink) samples (HSI cubes, each cube is of size 1000 × 512 × 224, in which 1000 × 512 are the spatial dimensions and 224 spectral bands) were deposited on three substrates (white cotton fabric, white tile, and PVC wall sheet). The samples are imaged for up to three days to include aging. Savitzky Golay filtering has been used to highlight the subtle bands of all samples, particularly the aged ones. Based on the derivative spectrum, important spectral bands were selected to train five different classifiers (SVM, ANN, KNN, Random Forest, and Decision Tree). The comparative analysis reveals that the proposed method outperformed several state-of-the-art methods. Full article

Super-resolution (SR) is significant for hyperspectral image (HSI) applications. In single-frame HSI SR, how to reconstruct detailed image structures in high resolution (HR) HSI is challenging since there is no auxiliary image (e.g., HR multispectral image) providing structural information. Wavelet could capture image structures in different orientations, and emphasis on predicting high-frequency wavelet sub-bands is helpful for recovering the detailed structures in HSI SR. In this study, we propose a multi-scale wavelet 3D convolutional neural network (MW-3D-CNN) for HSI SR, which predicts the wavelet coefficients of HR HSI rather than directly reconstructing the HR HSI. To exploit the correlation in the spectral and spatial domains, the MW-3D-CNN is built with 3D convolutional layers. An embedding subnet and a predicting subnet constitute the MW-3D-CNN, the embedding subnet extracts deep spatial-spectral features from the low resolution (LR) HSI and represents the LR HSI as a set of feature cubes. The feature cubes are then fed to the predicting subnet. There are multiple output branches in the predicting subnet, each of which corresponds to one wavelet sub-band and predicts the wavelet coefficients of HR HSI. The HR HSI can be obtained by applying inverse wavelet transform to the predicted wavelet coefficients. In the training stage, we propose to train the MW-3D-CNN with L1 norm loss, which is more suitable than the conventional L2 norm loss for penalizing the errors in different wavelet sub-bands. Experiments on both simulated and real spaceborne HSI demonstrate that the proposed algorithm is competitive with other state-of-the-art HSI SR methods. Full article

Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR methods consider the sparsity prior in spatial and spectral vector domains via vectorizing hyperspectral cubes along a certain dimension. Besides, in most previous works, little attention has been paid to exploiting the underlying nonlocal structure in spatial domain of the HSI. In this paper, we propose a nonlocal tensor sparse and low-rank regularization (NTSRLR) approach, which can encode essential structured sparsity of an HSI and explore its advantages for HSI-CSR task. Specifically, we study how to utilize reasonably the $l_1$ -based sparsity of core tensor and tensor nuclear norm function as tensor sparse and low-rank regularization, respectively, to describe the nonlocal spatial-spectral correlation hidden in an HSI. To study the minimization problem of the proposed algorithm, we design a fast implementation strategy based on the alternative direction multiplier method (ADMM) technique. Experimental results on various HSI datasets verify that the proposed HSI-CSR algorithm can significantly outperform existing state-of-the-art CSR techniques for HSI recovery. Full article

Collection of spectroradiometric measurements with associated biophysical variables is an essential part of the development and validation of optical remote sensing vegetation products. However, their quality can only be assessed in the subsequent analysis, and often there is a need for collecting extra data, e.g., to fill in gaps. To generate empirical-like surface reflectance data of vegetated surfaces, we propose to exploit emulation, i.e., reconstruction of spectral measurements through statistical learning. We evaluated emulation against classical interpolation methods using an empirical field dataset with associated hyperspectral spaceborne CHRIS and airborne HyMap reflectance spectra, to produce synthetic CHRIS and HyMap reflectance spectra for any combination of input biophysical variables. Results indicate that: (1) emulation produces surface reflectance data more accurately than interpolation when validating against a separate part of the field dataset; and (2) emulation produces the spectra multiple times (tens to hundreds) faster than interpolation. This technique opens various data processing opportunities, e.g., emulators not only allow rapidly producing large synthetic spectral datasets, but they can also speed up computationally intensive processing routines such as synthetic scene generation. To demonstrate this, emulators were run to simulate hyperspectral imagery based on input maps of a few biophysical variables coming from CHRIS, HyMap and Sentinel-2 (S2). The emulators produced spaceborne CHRIS-like and airborne HyMap-like surface reflectance imagery in the order of seconds, thereby approximating the spectra of vegetated surfaces sufficiently similar to the reference images. Similarly, it took a few minutes to produce a hyperspectral data cube with a spatial texture of S2 and a spectral resolution of HyMap. Full article

Known to be structured in several patterns at the same time, the prior image of interest is always modeled with the idea of enforcing multiple constraints on unknown signals. For instance, when dealing with a hyperspectral restoration problem, the combination of constraints with piece-wise smoothness and low rank has yielded promising reconstruction results. In this paper, we propose a novel mixed-noise removal method by employing 3D anisotropic total variation and low rank constraints simultaneously for the problem of hyperspectral image (HSI) restoration. The main idea of the proposed method is based on the assumption that the spectra in an HSI lies in the same low rank subspace and both spatial and spectral domains exhibit the property of piecewise smoothness. The low rankness of an HSI is approximately exploited by the nuclear norm, while the spectral-spatial smoothness is explored using 3D anisotropic total variation (3DATV), which is defined as a combination of 2D spatial TV and 1D spectral TV of the HSI cube. Finally, the proposed restoration model is effectively solved by the alternating direction method of multipliers (ADMM). Experimental results of both simulated and real HSI datasets validate the superior performance of the proposed method in terms of quantitative assessment and visual quality. Full article

Hyperspectral imagery has proven its potential in many research applications, especially in the field of environmental sciences. Currently, hyperspectral imaging is generally performed by satellite or aircraft platforms, but mini-UAV (Unmanned Aerial Vehicle) platforms (<20 kg) are now emerging. On such platforms, payload restrictions are critical, so sensors must be selected according to stringent specifications. This article presents the integration of a light pushbroom hyperspectral sensor onboard a multirotor UAV, which we have called Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations). This article depicts the system design: the UAV platform, the imaging module, the navigation module, and the interfacing between the different elements. Pushbroom sensors offer a better combination of spatial and spectral resolution than full-frame cameras. Nevertheless, data georectification has to be performed line by line, the quality of direct georeferencing being limited by mechanical stability, good timing accuracy, and the resolution and accuracy of the proprioceptive sensors. A georegistration procedure is proposed for geometrical pre-processing of hyperspectral data. The specifications of Hyper-DRELIO surveys are described through two examples of surveys above coastal or inland waters, with different flight altitudes. This system can collect hyperspectral data in VNIR (Visible and Near InfraRed) domain above small study sites (up to about 4 ha) with both high spatial resolution (<10 cm) and high spectral resolution (1.85 nm) and with georectification accuracy on the order of 1 to 2 m. Full article