Spectral Detection: Technologies and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 13035

Special Issue Editors

School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
Interests: chemometrics methods; rapid nondestructive detection of edible oil; quality control of traditional chinese medicine; near infrared spectral analysis; raman spectral analysis; ultraviolet-visible spectral analysis
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: laser absorption spectroscopy; solid-state lasers; laser micromachining
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: spectral imaging; spectral image processing; Raman spectral analysis; fourier transform spectrometry

Special Issue Information

Dear Colleagues,

With its non-contact, fast, efficient and dynamic characteristics, spectral detection has attracted much attention in recent years. Featured by its requirement in neither sampling nor post-sample processing, spectral detection technology has a wide range of applications in many fields, such as national defense, space remote sensing, food inspection, biomedicine, engineering, etc. Noticing the rapid expansion in studies concerning spectral detection, we are announcing a Special Issue entitled “Spectral Detection: Techniques and Applications” in Applied Sciences. This Special Issue aims to investigate the latest advances and the applications of spectral detection, with the topics of interest including, but not limited to, the following:

  • Multispectral and/or Hyperspectral detection;
  • Near-infrared and/or infrared spectral analysis;
  • Raman spectral detection;
  • Laser spectroscopic detection;
  • Fourier transform spectrometry and analysis;
  • Spectral sensing and/or imaging;
  • Chemometric methods and their application in spectral analysis.

Dr. Xihui Bian
Dr. Jin Yu
Dr. Qunbo Lv
Guest Editors

Manuscript Submission Information

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Keywords

  • chemometrics
  • multispectral detection
  • hyper-spectral detection
  • spectral analysis
  • spectral imaging
  • spectral sensing

Published Papers (14 papers)

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Research

13 pages, 6420 KiB  
Article
Unmanned Helicopter Airborne Fourier Transform Infrared Spectrometer Remote Sensing System for Hazardous Vapors Detection
by Zhengyang Shi, Min Huang, Lulu Qian, Wei Han, Guifeng Zhang and Xiangning Lu
Appl. Sci. 2024, 14(4), 1367; https://doi.org/10.3390/app14041367 - 07 Feb 2024
Viewed by 514
Abstract
The rapid development of unmanned aerial vehicles (UAVs) provides a new application mode for gas remote sensing. Compared with fixed observation and vehicle-mounted platforms, a Fourier transform infrared spectrometer (FTIR) integrated in the UAV can monitor chemical gases across a large area, can [...] Read more.
The rapid development of unmanned aerial vehicles (UAVs) provides a new application mode for gas remote sensing. Compared with fixed observation and vehicle-mounted platforms, a Fourier transform infrared spectrometer (FTIR) integrated in the UAV can monitor chemical gases across a large area, can collect data from multiple angles in three-dimensional space, and can operate in contaminated or hazardous environments. The unmanned helicopter has a larger payload and longer endurance than the rotary-wing drone, which relaxes the weight, size and power consumption limitations of the spectrometer. A FTIR remote sensing system integrated in an unmanned helicopter was developed. In order to solve the data acquisition and analysis problem caused by vibration and attitude instability of the unmanned helicopter, a dual-channel parallel oscillating mirror was designed to improve the stability of the interferometer module, and a robust principal component analysis algorithm based on kernel function was used to separate background spectrum and gas features. The flight experiment of sulfur hexafluoride gas detection was carried out. The results show that the system operates stably and can collect and identify the target spectrum in real time under the motion and hovering modes of an unmanned helicopter, which has broad application prospects. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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13 pages, 2314 KiB  
Article
Application of Near-Infrared Spectroscopy and Aquaphotomics in Understanding the Water Behavior during Cold Atmospheric Plasma Processing
by Junsha Luo, Tianao Xu, Wenshuo Ding, Xiaoying Wei, Hengchang Zang, Xiaolong Wang and Lian Li
Appl. Sci. 2024, 14(1), 1; https://doi.org/10.3390/app14010001 - 19 Dec 2023
Viewed by 759
Abstract
Plasma-activated water (PAW), obtained by exposing liquid to cold atmospheric plasma (CAP) for a period, has gained widespread attention for its potential as anti-bacterial, anti-infective, anti-cancer and other biological agents. It is important to understand the PAW behavior and express it in a [...] Read more.
Plasma-activated water (PAW), obtained by exposing liquid to cold atmospheric plasma (CAP) for a period, has gained widespread attention for its potential as anti-bacterial, anti-infective, anti-cancer and other biological agents. It is important to understand the PAW behavior and express it in a ‘visualization’ form. Near-infrared spectroscopy (NIR) and aquaphotomics were introduced in this study to investigate the PAW spectra to visualize the water molecular species and try to analyze the production and changes of the active substances in PAW. Second-order derivative, PCA and PLS were applied to identify specific peaks to construct the aquagram and reference method for the ROS assay used to prove the spectral results. The results showed that a longer treatment time resulted in greater spectral changes which could be visualized with 12 water matrix coordinates (WAMACS) and the change trends were in accordance with the ROS concentration variations. Furthermore, during PAW sample storage, there were fluctuations in spectral changes, with a general trend of increase, and a gradual decrease in ROS concentration due to active substance reactions in PAW. In conclusion, this study presents a new perspective on examining the water behavior of PAW and offers a new method to explore cold plasma biomedical materials. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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19 pages, 1998 KiB  
Article
Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
by Zekun Li, Jin Pan, Peidong He, Ziqi Zhang, Chunlu Zhao and Bing Li
Appl. Sci. 2023, 13(23), 12639; https://doi.org/10.3390/app132312639 - 24 Nov 2023
Viewed by 557
Abstract
Scale variation presents a significant challenge in object detection. To address this, multi-level feature fusion techniques have been proposed, exemplified by methods such as the feature pyramid network (FPN) and its extensions. Nonetheless, the input features provided to these methods and the interaction [...] Read more.
Scale variation presents a significant challenge in object detection. To address this, multi-level feature fusion techniques have been proposed, exemplified by methods such as the feature pyramid network (FPN) and its extensions. Nonetheless, the input features provided to these methods and the interaction among features across different levels are limited and inflexible. In order to fully leverage the features of multi-scale objects and amplify feature interaction and representation, we introduce a novel and efficient framework known as a multi-resolution and semantic-aware bidirectional adapter (MSBA). Specifically, MSBA comprises three successive components: multi-resolution cascaded fusion (MCF), a semantic-aware refinement transformer (SRT), and bidirectional fine-grained interaction (BFI). MCF adaptively extracts multi-level features to enable cascaded fusion. Subsequently, SRT enriches the long-range semantic information within high-level features. Following this, BFI facilitates ample fine-grained interaction via bidirectional guidance. Benefiting from the coarse-to-fine process, we can acquire robust multi-scale representations for a variety of objects. Each component can be individually integrated into different backbone architectures. Experimental results substantiate the superiority of our approach and validate the efficacy of each proposed module. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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17 pages, 4322 KiB  
Article
Near-Infrared Spectroscopy Coupled with a Neighborhood Rough Set Algorithm for Identifying the Storage Status of Paddy
by Dong Yang, Yuxing Zhou, Qianqian Li, Yu Jie and Tianyu Shi
Appl. Sci. 2023, 13(20), 11357; https://doi.org/10.3390/app132011357 - 16 Oct 2023
Viewed by 662
Abstract
Rapid and non-destructive identification of the suitable storage status of paddy during storage is crucial for controlling the quality of stored grains, which can provide high-quality raw grains for rice processing. Near-infrared (NIR) spectroscopy combined with neighborhood rough set (NRS) and multiple classification [...] Read more.
Rapid and non-destructive identification of the suitable storage status of paddy during storage is crucial for controlling the quality of stored grains, which can provide high-quality raw grains for rice processing. Near-infrared (NIR) spectroscopy combined with neighborhood rough set (NRS) and multiple classification methods were used to identify the different storage statuses of paddy. The NIR data were collected in the range of 1000–1800 nm, and three storage statuses from suitable storage to severely unsuitable storage were divided using the measured fatty acid value of paddy. The spectral features were selected using NRS, successive projection algorithm and variable combination population analysis methods. Random forest (RF), extreme learning machine, and soft independent modeling of class analogy classifiers coupled with spectral features were used to establish classification models to distinguish the different storage statuses of paddy. The comparison results indicated that the optimal wavelengths selected by NRS combined with the RF classifier to construct the NRS-RF series models led to satisfactory identification results, with high correct classification rates of 96.31% and 93.68% in the calibration and test sets, respectively; the indicators of sensitivity and specificity ranged from 0.93 to 0.99. Therefore, the combination of NIR technology with NRS and RF algorithms for identifying the storage status of paddy was feasible, as this would be more helpful for rapidly evaluating the changes of stored paddy quality. The proposed method from this study is expected to provide support for the development of non-destructive equipment for the accurate detection of the quality of stored paddy. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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13 pages, 3406 KiB  
Article
Quantitative Analysis of Biodiesel Adulterants Using Raman Spectroscopy Combined with Synergy Interval Partial Least Squares (siPLS) Algorithms
by Yuemei Su, Maogang Li, Chunhua Yan, Tianlong Zhang, Hongsheng Tang and Hua Li
Appl. Sci. 2023, 13(20), 11306; https://doi.org/10.3390/app132011306 - 14 Oct 2023
Viewed by 972
Abstract
Biodiesel has emerged as an alternative to traditional fuels with the aim of reducing the impact on the environment. It is produced by the esterification of oleaginous seeds, animal fats, etc., with short-chain alcohols in an alkaline solution, which is one of the [...] Read more.
Biodiesel has emerged as an alternative to traditional fuels with the aim of reducing the impact on the environment. It is produced by the esterification of oleaginous seeds, animal fats, etc., with short-chain alcohols in an alkaline solution, which is one of the most commonly used methods. This increases the oxygen content (from the fatty acids) and promotes the fuel to burn faster and more efficiently. The accurate quantification of biodiesel is of paramount importance to the fuel market due to the possibility of adulteration, which can result in economic losses, engine performance issues and environmental concerns related to corrosion. In response to achieving this goal, in this work, synergy interval partial least squares (siPLS) algorithms in combination with Raman spectroscopy are used for the quantification of the biodiesel content. Different pretreatment methods are discussed to eliminate a large amount of redundant information of the original spectrum. The siPLS technique for extracting feature variables is then used to optimize the input variables after pretreatment, in order to enhance the predictive performance of the calibration model. Finally, the D1-MSC-siPLS calibration model is constructed based on the preprocessed spectra, the selected input variables and the optimized model parameters. Compared with the feature variable selection methods of interval partial least squares (iPLS) and backward interval partial least squares (biPLS), results elucidate that the D1-MSC-siPLS calibration model is superior to the D1-MSC-biPLS and the D1-MSC-iPLS in the quantitative analysis of adulterated biodiesel. The D1-MSC-siPLS calibration model demonstrates better predictive performance compared to the full spectrum PLS model, with the optimal determination coefficient of prediction (R2P) being 0.9899; the mean relative error of prediction (MREP) decreased from 9.51% to 6.31% and the root--mean-squared error of prediction (RMSEP) decreased from 0.1912% (v/v) to 0.1367% (v/v), respectively. The above results indicate that Raman spectroscopy combined with the D1-MSC-siPLS calibration model is a feasible method for the quantitative analysis of biodiesel in adulterated hybrid fuels. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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14 pages, 3934 KiB  
Article
Quantitative Analysis of Coal Quality by a Portable Laser Induced Breakdown Spectroscopy and Three Chemometrics Methods
by Youquan Dou, Qingsong Wang, Sen Wang, Xi Shu, Minghui Ni and Yan Li
Appl. Sci. 2023, 13(18), 10049; https://doi.org/10.3390/app131810049 - 06 Sep 2023
Viewed by 829
Abstract
Laser-induced breakdown spectroscopy (LIBS) technology has the characteristics of small sample demand, simple sample preparation, simultaneous measurement of multiple elements and safety, which has great potential application in the rapid detection of coal quality. In this paper, 59 kinds of coal commonly used [...] Read more.
Laser-induced breakdown spectroscopy (LIBS) technology has the characteristics of small sample demand, simple sample preparation, simultaneous measurement of multiple elements and safety, which has great potential application in the rapid detection of coal quality. In this paper, 59 kinds of coal commonly used in Chinese power plants were tested by a lab-designed field-portable laser-induced breakdown spectrometer. The data set division methods and the quantitative analysis algorithm of ash content, volatile matter and calorific value of coal samples were carried out. The accuracy and prediction accuracy of three kinds of dataset partitioning methods, random selection (RS), Kennard–Stone (KS) and sample partitioning based on joint X-Y distances (SPXY), coupled with three quantitative algorithms, partial least squares regression (PLS), support vector machine regression (SVR) and random forest (RF), were compared and analyzed in this paper. The results show that the model featuring SPXY combined with RF has the best prediction performance. The R2 of ash content by the RF and SPXY method is 0.9843, the RMSEP of ash content is 1.3303 and the mean relative error (MRE) is 7.47%. The R2 of volatile matter is 0.9801, RMSEP is 0.7843 and MRE is 2.19%. The R2 of calorific value is 0.9844, RMSEP is 0.7324 and MRE is 2.27%. This study demonstrates that the field-portable LIBS device combining appropriate chemometrics algorithms has a wide application prospect in the rapid analysis of coal quality. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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10 pages, 1857 KiB  
Article
A Rapid and Nondestructive Detection Method for Rapeseed Quality Using NIR Hyperspectral Imaging Spectroscopy and Chemometrics
by Du Wang, Xue Li, Fei Ma, Li Yu, Wen Zhang, Jun Jiang, Liangxiao Zhang and Peiwu Li
Appl. Sci. 2023, 13(16), 9444; https://doi.org/10.3390/app13169444 - 21 Aug 2023
Viewed by 697
Abstract
In this study, a fast and non-destructive method was proposed to analyze rapeseed quality parameters with the help of NIR hyperspectral imaging spectroscopy and chemometrics. Hyperspectral images were acquired in the reflectance mode. Meanwhile, the region of interest was extracted from each image [...] Read more.
In this study, a fast and non-destructive method was proposed to analyze rapeseed quality parameters with the help of NIR hyperspectral imaging spectroscopy and chemometrics. Hyperspectral images were acquired in the reflectance mode. Meanwhile, the region of interest was extracted from each image by the regional growth algorithm. The kernel partial least square regression was used to build prediction models for crude protein content, oil content, erucic acid content, and glucosinolate content of rapeseed. The results showed that the correlation coefficients were 0.9461, 0.9503, 0.9572, and 0.9335, whereas the root mean square errors of prediction were 0.5514%, 0.5680%, 2.8113%, and 10.3209 µmol/g for crude protein content, oil content, erucic acid content, and glucosinolate content, respectively. It demonstrated that NIR hyperspectral imaging is a promising tool to determine rapeseed quality parameters in a rapid and non-invasive manner. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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14 pages, 2542 KiB  
Article
Development of Prediction Models for the Pasting Parameters of Rice Based on Near-Infrared and Machine Learning Tools
by Pedro Sousa Sampaio, Bruna Carbas and Carla Brites
Appl. Sci. 2023, 13(16), 9081; https://doi.org/10.3390/app13169081 - 09 Aug 2023
Cited by 1 | Viewed by 1131
Abstract
Due to the importance of rice (Oryza sativa) in food products, developing strategies to evaluate its quality based on a fast and reliable methodology is fundamental. Herein, near-infrared (NIR) spectroscopy combined with machine learning algorithms, such as interval partial least squares [...] Read more.
Due to the importance of rice (Oryza sativa) in food products, developing strategies to evaluate its quality based on a fast and reliable methodology is fundamental. Herein, near-infrared (NIR) spectroscopy combined with machine learning algorithms, such as interval partial least squares (iPLS), synergy interval PLS (siPLS), and artificial neural networks (ANNs), allowed for the development of prediction models of pasting parameters, such as the breakdown (BD), final viscosity (FV), pasting viscosity (PV), setback (ST), and trough (TR), from 166 rice samples. The models developed using iPLS and siPLS were characterized, respectively, by the following regression values: BD (R = 0.84; R = 0.88); FV (R = 0.57; R = 0.64); PV (R = 0.85; R = 0.90); ST (R = 0.85; R = 0.88); and TR (R = 0.85; R = 0.84). Meanwhile, ANN was also tested and allowed for a significant improvement in the models, characterized by the following values corresponding to the calibration and testing procedures: BD (Rcal = 0.99; Rtest = 0.70), FV (Rcal = 0.99; Rtest = 0.85), PV (Rcal = 0.99; Rtest = 0.80), ST (Rcal = 0.99; Rtest = 0.76), and TR (Rcal = 0.99; Rtest = 0.72). Each model was characterized by a specific spectral region that presented significative influence in terms of the pasting parameters. The machine learning models developed for these pasting parameters represent a significant tool for rice quality evaluation and will have an important influence on the rice value chain, since breeding programs focus on the evaluation of rice quality. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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19 pages, 9043 KiB  
Article
On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
by Yejin Li, Peng Rao, Zhengda Li and Jianliang Ai
Appl. Sci. 2023, 13(12), 6931; https://doi.org/10.3390/app13126931 - 08 Jun 2023
Viewed by 783
Abstract
Frequent aviation safety accidents of civil aircraft misses and crashes lead to an urgent need for flight safety assurance. Due to long-time flights over different backgrounds, accompanied by the changes in flight altitude and speed, it is difficult for a conventional space-based infrared [...] Read more.
Frequent aviation safety accidents of civil aircraft misses and crashes lead to an urgent need for flight safety assurance. Due to long-time flights over different backgrounds, accompanied by the changes in flight altitude and speed, it is difficult for a conventional space-based infrared detection system to use a set of fixed parameters to meet the stable detection requirement. To enhance the awareness of civil aircraft surveillance, a real-time parameter optimization method based on Automatic Dependent Surveillance-Broadcast (ADS-B) data is proposed. According to the background spectral characteristics and the real-time flight data, the most reasonable spectral band is analyzed, using the joint signal-to-noise/clutter ratio (JSNCR) as the evaluation criteria. Then, an automatic parameter adjustment is used to maximize the integration time and switch the integration capacitor gear. Numerical simulation results show that the JSNCR increased by 1.16 to 1.31 times, and the corresponding noise equivalent target radiant intensity (NET) reduced from 2.4 W/Sr to 1.2 W/Sr compared with a conventional fixed-parameter detection system. This study lays a solid theoretical foundation for the spectral band analysis of space-based AVD system design. Meanwhile, the proposed method can be used as a standard procedure to improve on-board performance. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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11 pages, 499 KiB  
Article
A Multidimensional Spectral Transformer with Channel-Wise Correlation for Hyperspectral Image Classification
by Kai Zhang, Zheng Tan, Jianying Sun, Baoyu Zhu, Yuanbo Yang and Qunbo Lv
Appl. Sci. 2023, 13(9), 5482; https://doi.org/10.3390/app13095482 - 28 Apr 2023
Viewed by 803
Abstract
Convolutional neural networks (CNNs) have been developed as an effective strategy for hyperspectral image (HSI) classification. However, the lack of feature extraction by CNN networks is due to the network failing to effectively extract global features and poor capability in distinguishing between different [...] Read more.
Convolutional neural networks (CNNs) have been developed as an effective strategy for hyperspectral image (HSI) classification. However, the lack of feature extraction by CNN networks is due to the network failing to effectively extract global features and poor capability in distinguishing between different feature categories that are similar. In order to solve these problems, this paper proposes a novel approach to hyperspectral image classification using a multidimensional spectral transformer with channel-wise correlation. The proposed method consists of two key components: an input mask and a channel correlation block. The input mask is used to extract relevant spectral information from hyperspectral images and discard irrelevant information, reducing the dimensionality of the input data and improving classification accuracy. The channel correlation block captures the correlations between different spectral channels and is integrated into the transformer network to improve the model’s discrimination power. The experimental results demonstrate that the proposed method achieves great performance with several benchmark hyperspectral image datasets. The input mask and channel correlation block effectively improve classification accuracy and reduce computational complexity. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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11 pages, 2574 KiB  
Article
On the Possibility of Universal Chemometric Calibration in X-ray Fluorescence Spectrometry: Case Study with Ore and Steel Samples
by Zahars Selivanovs, Vitaly Panchuk and Dmitry Kirsanov
Appl. Sci. 2023, 13(9), 5415; https://doi.org/10.3390/app13095415 - 26 Apr 2023
Viewed by 874
Abstract
The accuracy of X-ray fluorescence spectrometry in quantitative element analysis depends on the particular sample composition (so-called matrix effects). Counteracting these effects requires a large number of calibration samples similar in composition to those under analysis. Application of the model constructed for a [...] Read more.
The accuracy of X-ray fluorescence spectrometry in quantitative element analysis depends on the particular sample composition (so-called matrix effects). Counteracting these effects requires a large number of calibration samples similar in composition to those under analysis. Application of the model constructed for a particular type of samples is not possible for the analysis of samples having a different matrix composition. A possible solution for this problem can be found in the construction of universal calibration models. We propose the development of these universal models using chemometric tools: influence coefficients—partial least squares regression (IC-PLS) and nonlinear kernel regularized least squares regression. We hypothesize that the application of these methods for constructing calibration models would allow embracing the samples of different types in the framework of a single model. We explored this approach for the case of two substantially different types of samples: ores and steels. The performance of these methods was compared with the fundamental parameters (FP) method, which takes into account matrix effects using theoretical equations and allows handling samples of different elemental composition. IC-PLS significantly outperforms traditional FP in terms of accuracy for predicting the content of Al (root mean squared error of prediction 0.96% vs. 3.87%) and Ti (0.05% vs. 0.09%) and yields comparable results for Si and Mn quantification in ores and steels. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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15 pages, 3907 KiB  
Article
A Multi-Branch Training and Parameter-Reconstructed Neural Network for Assessment of Signal-to-Noise Ratio of Optical Remote Sensor on Orbit
by Bo Zhu, Xiaoning Lv, Congao Tan, Yuli Xia and Junsuo Zhao
Appl. Sci. 2023, 13(5), 2851; https://doi.org/10.3390/app13052851 - 22 Feb 2023
Viewed by 1108
Abstract
Signal-to-Noise Ratio (SNR) is the benchmark to evaluate the quality of optical remote sensors. For SNR estimation, most of the traditional methods have complicated processes, low efficiency, and general accuracy. In particular, they are not suitable for the distributed computation on intelligent satellites. [...] Read more.
Signal-to-Noise Ratio (SNR) is the benchmark to evaluate the quality of optical remote sensors. For SNR estimation, most of the traditional methods have complicated processes, low efficiency, and general accuracy. In particular, they are not suitable for the distributed computation on intelligent satellites. Therefore, an intelligent SNR estimation algorithm with strong computing power and more accuracy is urgently needed. Considering the simplicity of distributed deployment and the lightweight goal, our first proposition is to design a convolutional neural network (CNN) similar to VGG (proposed by Visual Geometry Group) to estimate SNR for optical remote sensors. In addition, considering the advantages of multi-branch structures, the second proposition is to train the CNN in a novel method of multi-branch training and parameter-reconstructed inference. In this study, simulated and real remote sensing images with different ground features are utilized to validate the effectiveness of our model and the novel training method. The experimental results show that the novel training method enhances the fitting ability of the network, and the proposed CNN trained in this method has high accuracy and reliable SNR estimation, which achieves a 3.9% RMSE for noise-level-known simulated images. When compared to the accuracy of the reference methods, such as the traditional and typical SNR methods and the denoising convolutional neural network (DnCNN), the performance of the proposed CNN trained in a novel method is the best, which achieves a relative error of 5.5% for hyperspectral images. The study is fit for optical remote sensing images with complicated ground surfaces and different noise levels captured by different optical remote sensors. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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18 pages, 4648 KiB  
Article
Super-Resolution Multicomponent Joint-Interferometric Fabry–Perot-Based Technique
by Yu Zhang, Qunbo Lv, Yinhui Tang, Peidong He, Baoyv Zhu, Xuefu Sui, Yuanbo Yang, Yang Bai and Yangyang Liu
Appl. Sci. 2023, 13(2), 1012; https://doi.org/10.3390/app13021012 - 11 Jan 2023
Viewed by 1040
Abstract
We propose a new spectral super-resolution technique combined with a Fabry–Perot interferometer (FPI) and an interferometric hyperspectral imager. To overcome the limitation of the maximal optical path difference (OPD) on the spectral resolution, the object spectrum is periodically modulated based on the FPI, [...] Read more.
We propose a new spectral super-resolution technique combined with a Fabry–Perot interferometer (FPI) and an interferometric hyperspectral imager. To overcome the limitation of the maximal optical path difference (OPD) on the spectral resolution, the object spectrum is periodically modulated based on the FPI, and an optical Fourier transform of the modulated spectrum information is performed using a double-beam interferometer to obtain an interferogram. Drawing on the concept of nonlinear structured light microscopy, the displacement of the high-frequency interference information in the interferogram after adding the FPI is analyzed to restore the high-frequency interference information and improve the spectral resolution. The optical system has a compact structure with little impact on complexity, spectral range, or luminous flux. Our simulation results show that this method can realize multicomponent joint-interference imaging to obtain spectral super-resolution information. The effects of the FPI’s reflectance and interval are analyzed, and the reflectance needs to be within 20~80% and the interval must be as close as possible to the maximum optical range of the interferometer. Compared with previous, related innovations, this innovation has the advantages of higher system stability, higher data utilization, and better suitability for interferometric imaging spectrometers. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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10 pages, 467 KiB  
Article
Construction and Application of Detection Model for Leucine and Tyrosine Content in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy
by Liwei Zhu, Rebecca Njeri Damaris, Yong Lv, Qianxi Du, Taoxiong Shi, Jiao Deng and Qingfu Chen
Appl. Sci. 2022, 12(21), 11051; https://doi.org/10.3390/app122111051 - 31 Oct 2022
Viewed by 1114
Abstract
To meet the demand of the breeding and processing industry of Golden Tartary buckwheat, quantitative identification models were established to test the content of leucine (Leu) and tyrosine (Tyr) in Golden Tartary buckwheat leaves by near-infrared reflectance spectroscopy (NIRS) with quantitative partial least [...] Read more.
To meet the demand of the breeding and processing industry of Golden Tartary buckwheat, quantitative identification models were established to test the content of leucine (Leu) and tyrosine (Tyr) in Golden Tartary buckwheat leaves by near-infrared reflectance spectroscopy (NIRS) with quantitative partial least squares (PLS). Leu’s modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–9000 cm−1 was appropriate for modeling (calibration sets: validation set = 6:1), the mean coefficient of determination (R2), standard error of calibration (SEC), and relative standard deviation (RSD) for the calibration set were 0.9229, 0.45, and 3.45%, respectively; for the validation set, the mean R2, SEC, and RSD were 0.9502, 0.47, and 3.65%, respectively. Tyr modeling results were as follows: first derivative (11) pretreatment, the wavenumber range of 4000–10,000 cm−1 was suitable for modeling (calibration sets: validation set = 4:1), the R2, SEC, and RSD for the calibration set was 0.9016, 0.15, and 5.72%, respectively; for the validation set, the mean R2, SEC, and RSD were 0.9012, 0.15, and 5.53%, respectively. It was proved that the Leu and Tyr content of Golden Tartary buckwheat could be quantified using the model structured by near infrared spectroscopy combined with the partial least squares method. Full article
(This article belongs to the Special Issue Spectral Detection: Technologies and Applications)
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