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High Throughput AOTF Hyperspectral Imager for Randomly Polarized Light
摘要： The acousto-optic tunable filter (AOTF) is one of the most used techniques for hyperspectral imaging (HSI), and is capable of fast and random wavelength access, high diffraction efficiency, and good spectral resolution. Typical AOTF-HSI works with linearly polarized light; hence, its throughput is limited for randomly polarized applications such as fluorescence imaging. We report an AOTF-based imager design using both polarized components of the input light. The imager is designed to operate in the 450 to 800 nm region with resolutions in the range of 1.5–4 nm. The performance characterization results show that this design leads to 68% improvement in throughput for randomly polarized light. We also compared its performance against a liquid crystal tunable filter (LCTF)-based imager.
关键词： hyperspectral imaging,LCTF,AOTF,optical throughput
更新于2025-09-23 15:22:29
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Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution
摘要： Hyperspectral images (HSIs) with high spectral resolution only have the low spatial resolution. On the contrary, multispectral images (MSIs) with much lower spectral resolution can be obtained with higher spatial resolution. Therefore, fusing the high-spatial-resolution MSI (HR-MSI) with low-spatial-resolution HSI of the same scene has become the very popular HSI super-resolution scheme. In this paper, a novel low tensor-train (TT) rank (LTTR)-based HSI super-resolution method is proposed, where an LTTR prior is designed to learn the correlations among the spatial, spectral, and nonlocal modes of the nonlocal similar high-spatial-resolution HSI (HR-HSI) cubes. First, we cluster the HR-MSI cubes as many groups based on their similarities, and the HR-HSI cubes are also clustered according to the learned cluster structure in the HR-MSI cubes. The HR-HSI cubes in each group are much similar to each other and can constitute a 4-D tensor, whose four modes are highly correlated. Therefore, we impose the LTTR constraint on these 4-D tensors, which can effectively learn the correlations among the spatial, spectral, and nonlocal modes because of the well-balanced matricization scheme of TT rank. We formulate the super-resolution problem as TT rank regularized optimization problem, which is solved via the scheme of alternating direction method of multipliers. Experiments on HSI data sets indicate the effectiveness of the LTTR-based method.
关键词： low tensor-train (TT) rank (LTTR) learning,image fusion,Hyperspectral imaging,superresolution
更新于2025-09-23 15:22:29
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A Coarse-to-Fine Optimization for Hyperspectral Band Selection
摘要： Hyperspectral band selection is a feature selection method that selects a most representative set of bands to achieve a good performance in several tasks such as classification and anomaly detection. It reduces the burden of storage, transmission, and computation. In this letter, a two-stage band selection algorithm is introduced. It selects bands and refines the result using a linear reconstruction error criterion. Then a coarse-to-fine band selection (CFBS) strategy is applied to the two-stage band selection in order to achieve a better result. CFBS selects bands group by group. Each group is selected based on bands that are not well represented by the previous groups, trying to minimize the linear reconstruction error. Experiments show that the proposed method has a significant advancement compared with other competitors.
关键词： Band selection,hyperspectral imaging.
更新于2025-09-23 15:22:29
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A Nondestructive Real-Time Detection Method of Total Viable Count in Pork by Hyperspectral Imaging Technique
摘要： A nondestructive method was developed for assessing total viable count (TVC) in pork during refrigerated storage by using hyperspectral imaging technique in this study. The hyperspectral images in the visible/near-infrared (VIS/NIR) region of 400–1100 nm were acquired for fifty pork samples, and their VIS/NIR diffuse reflectance spectra were extracted from the images. The reference values of TVC in pork samples were determined by classical microbiological plating method. Both partial least square regression (PLSR) model and support vector machine regression model (SVR) of TVC were built for comparative analysis to achieve better results. Different transformation methods and filtering methods were applied to improve the models. The results show that both the optimized PLSR model and SVR model can predict the TVC very well, while the SVR model based on second derivation was better, which achieved with RP (correlation coefficient of prediction set) = 0.94 and SEP (standard error of prediction set) = 0.4570 log CFU/g in the prediction set. An image processing algorithm was then developed to transfer the prediction model to every pixel of the image of the entire sample; the visualizing map of TVC would be displayed in real-time during the detection process due to the simplicity of the model. The results demonstrated that hyperspectral imaging is a potential reliable approach for non-destructive and real-time prediction of TVC in pork.
关键词： visible/near-infrared,total viable count,pork,hyperspectral imaging
更新于2025-09-23 15:22:29
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Nonconvex-sparsity and Nonlocal-smoothness Based Blind Hyperspectral Unmixing
摘要： Blind hyperspectral unmixing (HU), as a crucial technique for hyperspectral data exploitation, aims to decompose mixed pixels into a collection of constituent materials weighted by the corresponding fractional abundances. In recent years, nonnegative matrix factorization (NMF) based methods have become more and more popular for this task and achieved promising performance. Among these methods, two types of properties upon the abundances, namely the sparseness and the structural smoothness, have been explored and shown to be important for blind HU. However, all of previous methods ignores another important insightful property possessed by a natural hyperspectral images (HSI), non-local smoothness, which means that similar patches in a larger region of an HSI are sharing the similar smoothness structure. Based on previous attempts on other tasks, such a prior structure reflects intrinsic configurations underlying a HSI, and is thus expected to largely improve the performance of the investigated HU problem. In this paper, we firstly consider such prior in HSI by encoding it as the non-local total variation (NLTV) regularizer. Furthermore, by fully exploring the intrinsic structure of HSI, we generalize NLTV to non-local HSI TV (NLHTV) to make the model more suitable for the bind HU task. By incorporating these two regularizers, together with a non-convex log-sum form regularizer characterizing the sparseness of abundance maps, to the NMF model, we propose novel blind HU models named NLTV/NLHTV and log-sum regularized NMF (NLTV-LSRNMF/NLHTV-LSRNMF), respectively. To solve the proposed models, an efficient algorithm is designed based on alternative optimization strategy (AOS) and alternating direction method of multipliers (ADMM). Extensive experiments conducted on both simulated and real hyperspectral data sets substantiate the superiority of the proposed approach over other competing ones for blind HU task.
关键词： log-sum penalty,non-negative matrix factorization,non-local total variation regularization,blind unmixing,Hyperspectral imaging
更新于2025-09-23 15:22:29
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An Outlier-insensitive Unmixing Algorithm with Spatially Varying Hyperspectral Signatures
摘要： Effective hyperspectral unmixing (HU) is essential to the estimation of the underlying materials’ signatures (endmember signatures) and their spatial distributions (abundance maps) from a given image (data) of a hyperspectral scene. Recently, investigating HU under the non-negligible endmember variability (EV) and outlier effects (OE) has drawn extensive attention. Some state-of-the-art works either consider EV or consider OE, but none of them considers both EV and OE simultaneously. In this paper, we propose a novel HU algorithm, referred to as the variability/outlier-insensitive multi-convex unmixing (VOIMU) algorithm, that is robust against both EV and OE. Considering two suitable regularizers, a nonconvex minimization problem is formulated for which the perturbed linear mixing model (PLMM) proposed by Thouvenin et al., is used for modeling EV, while OE is implicitly handled by applying a p quasi-norm to the data fitting with 0 < p < 1. Then we reformulate it into a multi-convex problem which is then solved by the block coordinate decent (BCD) method, with convergence guarantee by casting it into the block successive upper bound minimization (BSUM) framework. The proposed VOIMU algorithm can yield a stationary-point solution with convergence guarantee, together with some intriguing information of potential outlier pixels though outliers are neither physically modeled in the above problem nor detected in the algorithm operation. Finally, we provide some simulation results and experimental results using real data to demonstrate the efficacy and practical applicability of the proposed VOIMU algorithm.
关键词： block successive upper bound minimization (BSUM),endmember variability,alternating direction method of multipliers (ADMM),outlier effects,block coordinate decent (BCD) method,Hyperspectral imaging
更新于2025-09-23 15:22:29
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Fluorescence Hyperspectral Imaging of Oil Samples and Its Quantitative Applications in Component Analysis and Thickness Estimation
摘要： The fast response and analysis of oil spill accidents is important but remains challenging. Here, a compact fluorescence hyperspectral system based on a grating-prism structure able to perform component analysis of oil as well as make a quantitative estimation of oil film thickness is developed. The spectrometer spectral range is 366–814 nm with a spectral resolution of 1 nm. The feasibility of the spectrometer system is demonstrated by determining the composition of three types of crude oil and various mixtures of them. The relationship between the oil film thickness and the fluorescent hyperspectral intensity is furthermore investigated and found to be linear, which demonstrates the feasibility of using the fluorescence data to quantitatively measure oil film thickness. Capable of oil identification, distribution analysis, and oil film thickness detection, the fluorescence hyperspectral imaging system presented is promising for use during oil spill accidents by mounting it on, e.g., an unmanned aerial vehicle.
关键词： K-means clustering,principal component analysis,fluorescence hyperspectral imaging,oil detection
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Geological Mapping of Hydrothermal Alteration on Volcanoes from Multi-Sensor Platforms
摘要： Hydrothermal alteration due to geothermal fluids often introduces mineral alteration and weathering that poses significant natural hazards around volcanoes. Hydrothermal alteration can be mapped remotely using satellite and airborne derive images. In this study, we explored the capacity of available multispectral satellites, high-resolution airborne hyperspectral and LiDAR imagery to provide an improved geological mapping and classification capability for volcanic terrains. Image classification experiments using a Random Forest approach trained using ground class data to classify 15 ground cover types show that Sentinel-2 and Landsat 8 OLI+TIR can provide a geological map with Overall (OA) and Kappa Accuracies (KA) of 69% and 66% respectively. Classification accuracy was dramatically improved when high-resolution airborne datasets were included. The use of full-spectrum AisaFENIX hyperspectral images improved accuracies to OA = 84% and KA = 82%. The maximum image classification accuracy is reached (OA = 87, KA = 85%) when all input features were combined.
关键词： Sentinel-2,volcano,geological mapping,Landsat 8,LiDAR,hyperspectral imaging
更新于2025-09-23 15:21:21
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Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging
摘要： Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874–1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated. Principal component analysis (PCA) and independent component analysis (ICA) of object-wise spectra and pixel-wise spectra were conducted to select effective wavelengths. Pixel-wise PCA scores images indicated differences between two varieties and among different grades. SVM (Support Vector Machine), k-NN (k-nearest Neighbors Algorithm), and RBFNN (Radial Basis Function Neural Network) models were built to discriminate two varieties of raisins. Results indicated that both SVM and RBFNN models based on object-wise spectra using optimal wavelengths selected by PCA could be used for raisin variety identification. The visualization maps verified the effectiveness of using hyperspectral imaging to identify raisin varieties.
关键词： object-wise,pixel-wise,support vector machine,near-infrared hyperspectral imaging,raisins
更新于2025-09-23 15:21:21
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Non-Lorentzian Local Density of States in Coupled Photonic Crystal Cavities Probed by Near- and Far-Field Emission
摘要： Recent theories proposed a deep revision of the well-known expression for the Purcell factor, with counterintuitive effects, such as complex modal volumes and non-Lorentzian local density of states. We experimentally demonstrate these predictions in tailored coupled cavities on photonic crystal slabs with relatively low optical losses. Near-field hyperspectral imaging of quantum dot photoluminescence is proved to be a direct tool for measuring the line shape of the local density of states. The experimental results clearly evidence non-Lorentzian character, in perfect agreement with numerical and theoretical predictions. Spatial maps with deep subwavelength resolution of the real and imaginary parts of the complex mode volumes are presented. The generality of these results is confirmed by an additional set of far-field and time-resolved experiments in cavities with larger modal volume and higher quality factors.
关键词： non-Lorentzian local density of states,Purcell factor,quantum dot photoluminescence,photonic crystal cavities,near-field hyperspectral imaging
更新于2025-09-23 15:21:01