Journal articles on the topic 'Leafspec Hyperspectral Image Calibration'
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Mäkelä, Mikko, Paul Geladi, Marja Rissanen, Lauri Rautkari, and Olli Dahl. "Hyperspectral near infrared image calibration and regression." Analytica Chimica Acta 1105 (April 2020): 56–63. http://dx.doi.org/10.1016/j.aca.2020.01.019.
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Zhang, Xizhen, Aiwu Zhang, Mengnan Li, Lulu Liu, and Xiaoyan Kang. "Restoration and Calibration of Tilting Hyperspectral Super-Resolution Image." Sensors 20, no. 16 (August 15, 2020): 4589. http://dx.doi.org/10.3390/s20164589.
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Tilting sampling is a novel sampling mode for achieving a higher resolution of hyperspectral imagery. However, most studies on the tilting image have only focused on a single band, which loses the features of hyperspectral imagery. This study focuses on the restoration of tilting hyperspectral imagery and the practicality of its results. First, we reduced the huge data of tilting hyperspectral imagery by the p-value sparse matrix band selection method (pSMBS). Then, we restored the reduced imagery by optimal reciprocal cell combined modulation transfer function (MTF) method. Next, we built the relationship between the restored tilting image and the original normal image. We employed the least square method to solve the calibration equation for each band. Finally, the calibrated tilting image and original normal image were both classified by the unsupervised classification method (K-means) to confirm the practicality of calibrated tilting images in remote sensing applications. The results of classification demonstrate the optimal reciprocal cell combined MTF method can effectively restore the tilting image and the calibrated tiling image can be used in remote sensing applications. The restored and calibrated tilting image has a higher resolution and better spectral fidelity.
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Burger, James, and Paul Geladi. "Hyperspectral NIR image regression part I: calibration and correction." Journal of Chemometrics 19, no. 5-7 (May 2005): 355–63. http://dx.doi.org/10.1002/cem.938.
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Davies, Matthew, Mary B. Stuart, Matthew J. Hobbs, Andrew J. S. McGonigle, and Jon R. Willmott. "Image Correction and In Situ Spectral Calibration for Low-Cost, Smartphone Hyperspectral Imaging." Remote Sensing 14, no. 5 (February 25, 2022): 1152. http://dx.doi.org/10.3390/rs14051152.
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Developments in the portability of low-cost hyperspectral imaging instruments translate to significant benefits to agricultural industries and environmental monitoring applications. These advances can be further explicated by removing the need for complex post-processing and calibration. We propose a method for substantially increasing the utility of portable hyperspectral imaging. Vertical and horizontal spatial distortions introduced into images by ‘operator shake’ are corrected by an in-scene reference card with two spatial references. In situ light-source-independent spectral calibration is performed. This is achieved by a comparison of the ground-truth spectral reflectance of an in-scene red–green–blue target to the uncalibrated output of the hyperspectral data. Finally, bias introduced into the hyperspectral images due to the non-flat spectral output of the illumination is removed. This allows for low-skilled operation of a truly handheld, low-cost hyperspectral imager for agriculture, environmental monitoring, or other visible hyperspectral imaging applications.
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Jiang, Yonghua, Jingyin Wang, Li Zhang, Guo Zhang, Xin Li, and Jiaqi Wu. "Geometric Processing and Accuracy Verification of Zhuhai-1 Hyperspectral Satellites." Remote Sensing 11, no. 9 (April 26, 2019): 996. http://dx.doi.org/10.3390/rs11090996.
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The second batch of Zhuhai-1 microsatellites was successfully launched on 26 April 2018. The batch included four Orbita hyperspectral satellites (referred to as OHS-A, OHS-B, OHS-C, and OHS-D) and one video satellite (OVS-2A), which have excellent hyperspectral data acquisition abilities. For the first time in China, a number of hyperspectral satellite networks have been realized. To ensure the application of hyperspectral remote sensing data, a series of on-orbit geometry processing and accuracy verification studies has been carried out on the “Zhuhai-1” hyperspectral camera since the satellite was launched. This paper presents the geometric processing methods involved in the production of Zhuhai-1 hyperspectral satellite basic products, including geometric calibration and basic product production algorithms. The OHS images were used to perform on-orbit geometric calibration, and the calibration accuracy was better than 0.5 pixels. The registration accuracy of the image spectrum of the basic product after calibration, the single orientation accuracy, and the accuracy of the regional network adjustment were evaluated. The spectral registration accuracy of the OHS basic products is 0.3–0.5 pixels, which is equivalent to the spectral band calibration accuracy. The single orientation accuracy is better than 1.5 pixels and the regional network adjustment accuracy is better than 1.2 pixels. The generated area orthoimages meet the seamless edge requirements, which verifies that the OHS basic product image has good regional mapping capabilities and can meet the application requirements.
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Gorretta, Nathalie, Gilles Rabatel, Jean-Michel Roger, Christophe Fiorio, Camille Lelong, and Veronique Bellon-Maurel. "Hyperspectral Imaging System Calibration Using Image Translations and Fourier Transform." Journal of Near Infrared Spectroscopy 16, no. 4 (January 2008): 371–80. http://dx.doi.org/10.1255/jnirs.809.
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Aasen, H., J. Bendig, A. Bolten, S. Bennertz, M. Willkomm, and G. Bareth. "Introduction and preliminary results of a calibration for full-frame hyperspectral cameras to monitor agricultural crops with UAVs." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 1–8. http://dx.doi.org/10.5194/isprsarchives-xl-7-1-2014.
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Hyperspectral remote sensing helps to acquire information about the status of agricultural crops to allow optimized management practices in the context of precision agriculture. Due to technological innovations small and lightweight hyperspectral sensors have become available which may be carried by unmanned aerial vehicles (UAVs). In this paper we give a brief overview over existing hyperspectral sensors for UAVs. We focus on a new type of full-frame sensors which capture hyperspectral information in two dimensional image frames. We then develop a calibration procedure for these sensors and identify challenges in remote sensing of vegetation. The calibration is evaluate by in-field data acquired during a flight campaign. The spectral calibration shows good results with less than three percent difference in reflection for 110 of the 125 bands (458 to 886 nm).
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Lyu, S., C. Huang, and M. Hou. "REFLECTANCE RECONSTRUCTION OF HYPERSPECTRAL IMAGE BASED ON GAUSSIAN SURFACE FITTING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1365–69. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1365-2020.
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Abstract. Different from the field of remote sensing, artificial lights are often utilized as the energy source for spectral imaging in the ground hyperspectral applications. The kind of double-spot light source is widely adopted in some large scale ground hyperspectral applications. However, it is hard to reach a satisfied lighting without difference in light intensity in many cases although the lamps are tuned carefully. Therefore, a reflectance calibration of hyperspectral imaging based on the data of diffuse reflectance standard and Gaussian surface fitting is proposed in this paper. The purpose is to improve the reconstruction accuracy of hyperspectral reflectance image by minimized the error caused by the uneven illumination of artificial light source. The method has a higher accuracy than traditional one according to the experiment results.
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Chang, An Jin, Jae Wan Choi, Ah Ram Song, Ye Ji Kim, and Jin Ha Jung. "Vicarious Radiometric Calibration of RapidEye Satellite Image Using CASI Hyperspectral Data." Journal of Korean Society for Geospatial Information System 23, no. 3 (September 30, 2015): 3–10. http://dx.doi.org/10.7319/kogsis.2015.23.3.003.
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Krtalić, Andrija, Vanja Miljković, Dubravko Gajski, and Ivan Racetin. "Spatial Distortion Assessments of a Low-Cost Laboratory and Field Hyperspectral Imaging System." Sensors 19, no. 19 (October 1, 2019): 4267. http://dx.doi.org/10.3390/s19194267.
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This article describes the adaptation of an existing aerial hyperspectral imaging system in a low-cost setup for collecting hyperspectral data in laboratory and field environment and spatial distortion assessments. The imaging spectrometer system consists of an ImSpector V9 hyperspectral pushbroom scanner, PixelFly high performance digital CCD camera, and a subsystem for navigation, position determination and orientation of the system in space, a sensor bracket and control system. The main objective of the paper is to present the system, with all its limitations, and a spatial calibration method. The results of spatial calibration and calculation of modulation transfer function (MTF) are reported along with examples of images collected and potential uses in agronomy. The distortion value rises drastically at the edges of the image in the near-infrared segment, while the results of MTF calculation showed that the image sharpness was equal for the bands from the visible part of the spectrum, and approached Nyquist’s theory of digitalization. In the near-infrared part of the spectrum, the MTF values showed a less sharp decrease in comparison with the visible part. Preliminary image acquisition indicates that this hyperspectral system has potential in agronomic applications.
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Li, Xue-Ying, Guo-xing Ren, Ping-Ping Fan, Yan Liu, Zhong-Liang Sun, Guang-Li Hou, and Mei-Rong Lv. "Study on the Calibration Transfer of Soil Nutrient Concentration from the Hyperspectral Camera to the Normal Spectrometer." Journal of Spectroscopy 2020 (April 27, 2020): 1–10. http://dx.doi.org/10.1155/2020/8137142.
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The calibration transfer between instruments is mainly aimed at the calibration transfer between normal spectrometers. There are few studies on the calibration transfer of soil nutrient concentration from a hyperspectral camera to a normal spectrometer. In this paper, 164 soil samples from three regions in Qingdao, China, were collected. The spectral data of normal spectrometer and hyperspectral camera and the concentration of total carbon and nitrogen were obtained. And then, the models of soil total carbon and nitrogen content were established by using the spectral data of a normal spectrometer. The hyperspectral data were transferred by a variety of methods, such as single conventional calibration transfer algorithm, combination of multiple calibration transfer algorithms, and calibration transfer algorithm after spectral pretreatment. The transferred hyperspectral data were predicted by the total carbon and total nitrogen concentration model established by using a normal spectrometer. The absolute coefficients Rt2 and root mean square error of prediction (RMSEP) were used to evaluate the prediction performance after calibration transfer. After trying many calibration transfer methods, the prediction performance of calibration transfer by the Repfile-PDS and Repfile-SNV methods was the best. In the calibration transfer of the Repfile-PDS method, when the number of PDS windows was 27 and the number of standard data was 40, the Rt2 and the RMSEP of TC concentration were 0.627 and 2.351. When the number of PDS windows was 25 and the number of standard data was 100, the Rt2 and the RMSEP of TN concentration were 0.666 and 0.297. In the calibration transfer of the Repfile-SNV method, when the number of TC and TN standard data was 120, the Rt2 was the largest, 0.701 and 0.722, respectively, and the RMSEP was 2.880 and 0.399, respectively. After the hyperspectral data were calibration transferred by the above algorithms, they could be predicted by the soil TC and TN concentration model established by using a normal spectrometer, and better prediction results can be obtained. The solution of the calibration transfer of soil nutrient concentration from the hyperspectral camera to the normal spectrometer provides a powerful basis for rapid prediction of a large number of image information data collected by using a hyperspectral camera. It greatly reduces the workload and promotes the application of hyperspectral camera in quantitative analysis and rapid measurement technology.
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Shurygin, B., M. Shestakova, A. Nikolenko, E. Badasen, and P. Strakhov. "ACCOUNTING FOR VARIANCE IN HYPERSPECTRAL DATA COMING FROM LIMITATIONS OF THE IMAGING SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 365–69. http://dx.doi.org/10.5194/isprs-archives-xli-b7-365-2016.
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Over the course of the past few years, a number of methods was developed to incorporate hyperspectral imaging specifics into generic data mining techniques, traditionally used for hyperspectral data processing. Projection pursuit methods embody the largest class of methods empoyed for hyperspectral image data reduction, however, they all have certain drawbacks making them either hard to use or inefficient. It has been shown that hyperspectral image (HSI) statistics tend to display “heavy tails” (Manolakis2003)(Theiler2005), rendering most of the projection pursuit methods hard to use. Taking into consideration the magnitude of described deviations of observed data PDFs from normal distribution, it is apparent that <i>a priori</i> knowledge of variance in data caused by the imaging system is to be employed in order to efficiently classify objects on HSIs (Kerr, 2015), especially in cases of wildly varying SNR. A number of attempts to describe this variance and compensating techniques has been made (Aiazzi2006), however, new data quality standards are not yet set and accounting for the detector response is made under large set of assumptions. Current paper addresses the issue of hyperspectral image classification in the context of different variance sources based on the knowledge of calibration curves (both spectral and radiometric) obtained for each pixel of imaging camera. A camera produced by ZAO NPO Lepton (Russia) was calibrated and used to obtain a test image. <i>A priori</i> known values of SNR and spectral channel cross-correlation were incorporated into calculating test statistics used in dimensionality reduction and feature extraction. Expectation-Maximization classification algorithm modification for non-Gaussian model as described by (Veracini2010) was further employed. The impact of calibration data coarsening by ignoring non-uniformities on false alarm rate was studied. Case study shows both regions of scene-dominated variance and sensor-dominated variance, leading to different preprocession parameters and, ultimatively, classification results. A multilevel system for denoting hyperspectral pushbroom scanners calibration quality was proposed.
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Shurygin, B., M. Shestakova, A. Nikolenko, E. Badasen, and P. Strakhov. "ACCOUNTING FOR VARIANCE IN HYPERSPECTRAL DATA COMING FROM LIMITATIONS OF THE IMAGING SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 365–69. http://dx.doi.org/10.5194/isprsarchives-xli-b7-365-2016.
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Over the course of the past few years, a number of methods was developed to incorporate hyperspectral imaging specifics into generic data mining techniques, traditionally used for hyperspectral data processing. Projection pursuit methods embody the largest class of methods empoyed for hyperspectral image data reduction, however, they all have certain drawbacks making them either hard to use or inefficient. It has been shown that hyperspectral image (HSI) statistics tend to display “heavy tails” (Manolakis2003)(Theiler2005), rendering most of the projection pursuit methods hard to use. Taking into consideration the magnitude of described deviations of observed data PDFs from normal distribution, it is apparent that <i>a priori</i> knowledge of variance in data caused by the imaging system is to be employed in order to efficiently classify objects on HSIs (Kerr, 2015), especially in cases of wildly varying SNR. A number of attempts to describe this variance and compensating techniques has been made (Aiazzi2006), however, new data quality standards are not yet set and accounting for the detector response is made under large set of assumptions. Current paper addresses the issue of hyperspectral image classification in the context of different variance sources based on the knowledge of calibration curves (both spectral and radiometric) obtained for each pixel of imaging camera. A camera produced by ZAO NPO Lepton (Russia) was calibrated and used to obtain a test image. <i>A priori</i> known values of SNR and spectral channel cross-correlation were incorporated into calculating test statistics used in dimensionality reduction and feature extraction. Expectation-Maximization classification algorithm modification for non-Gaussian model as described by (Veracini2010) was further employed. The impact of calibration data coarsening by ignoring non-uniformities on false alarm rate was studied. Case study shows both regions of scene-dominated variance and sensor-dominated variance, leading to different preprocession parameters and, ultimatively, classification results. A multilevel system for denoting hyperspectral pushbroom scanners calibration quality was proposed.
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Ahmad, Muhammad, Manuel Mazzara, and Salvatore Distefano. "Regularized CNN Feature Hierarchy for Hyperspectral Image Classification." Remote Sensing 13, no. 12 (June 10, 2021): 2275. http://dx.doi.org/10.3390/rs13122275.
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Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) and are known to be effective in exploiting joint spatial-spectral information with the expense of lower generalization performance and learning speed due to the hard labels and non-uniform distribution over labels. Therefore, this paper proposed an idea to enhance the generalization performance of CNN for HSIC using soft labels that are a weighted average of the hard labels and uniform distribution over ground labels. The proposed method helps to prevent CNN from becoming over-confident. We empirically show that, in improving generalization performance, regularization also improves model calibration, which significantly improves beam-search. Several publicly available Hyperspectral datasets are used to validate the experimental evaluation, which reveals improved performance as compared to the state-of-the-art models with overall 99.29%, 99.97%, and 100.0% accuracy for Indiana Pines, Pavia University, and Salinas dataset, respectively.
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Chaity, Manisha Das, Morakot Kaewmanee, Larry Leigh, and Cibele Teixeira Pinto. "Hyperspectral Empirical Absolute Calibration Model Using Libya 4 Pseudo Invariant Calibration Site." Remote Sensing 13, no. 8 (April 15, 2021): 1538. http://dx.doi.org/10.3390/rs13081538.
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The objective of this paper is to find an empirical hyperspectral absolute calibration model using Libya 4 pseudo invariant calibration site (PICS). The approach involves using the Landsat 8 (L8) Operational Land Imager (OLI) as the reference radiometer and using Earth Observing One (EO-1) Hyperion, with a spectral resolution of 10 nm as a hyperspectral source. This model utilizes data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the Libya 4 PICS. It uses an improved, simple, empirical, hyperspectral Bidirectional Reflectance Distribution function (BRDF) model accounting for four angles: solar zenith and azimuth, and view zenith and azimuth angles. This model can perform absolute calibration in 1 nm spectral resolution by predicting TOA reflectance in all existing spectral bands of the sensors. The resultant model was validated with image data acquired from satellite sensors such as Landsat 7, Sentinel 2A, and Sentinel 2B, Terra MODIS, Aqua MODIS, from their launch date to 2020. These satellite sensors differ in terms of the width of their spectral bandpass, overpass time, off-nadir viewing capabilities, spatial resolution, and temporal revisit time, etc. The result demonstrates the efficacy of the proposed model has an accuracy of the order of 3% with a precision of about 3% for the nadir viewing sensors (with view zenith angle up to 5°) used in the study. For the off-nadir viewing satellites with view zenith angle up to 20°, it can have an estimated accuracy of 6% and precision of 4%.
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Marwaha, R., A. Kumar, P. L. N. Raju, and Y. V. N. Krishna Murthy. "Target detection algorithm for airborne thermal hyperspectral data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 827–32. http://dx.doi.org/10.5194/isprsarchives-xl-8-827-2014.
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Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm<sup>−1</sup> to 1280 cm<sup>−1</sup> region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm<sup>−1</sup> (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.
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Zhou, Xiao Hu. "Correcting Synchronous Scanning OMIS Remote Sensing Images Using the Spatial Orientation Data of the Inertial Navigation System." Applied Mechanics and Materials 513-517 (February 2014): 2867–70. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2867.
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Inertial navigation system using IMU (Inertial Measurement Unit) of the flying space positioning data POS (Position & Orientation System) synchronized scanning of the hyperspectral remote sensing OMIS (Operational Modular Imaging Spectrometer) image correction, obtaining from the IMU in sync with the attitude parameter OMIS , the coordinate transformation parameter calculation and flight attitude, according to OMIS imaging principle of mathematical calibration model, the corrected image pixel re-sampling, the image correction, and achieved better image processing results.
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Zhang, Yuechun, Jun Sun, Junyan Li, Xiaohong Wu, and Chunmei Dai. "Quantitative Analysis of Cadmium Content in Tomato Leaves Based on Hyperspectral Image and Feature Selection." Applied Engineering in Agriculture 34, no. 5 (2018): 789–98. http://dx.doi.org/10.13031/aea.12679.
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Abstract.In order to ensure that safe and healthy tomatoes can be provided to people, a method for quantitative determination of cadmium content in tomato leaves based on hyperspectral imaging technology was put forward in this study. Tomato leaves with seven cadmium stress gradients were studied. Hyperspectral images of all samples were firstly acquired by the hyperspectral imaging system, then the spectral data were extracted from the hyperspectral images. To simplify the model, three algorithms of competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and bootstrapping soft shrinkage (BOSS) were used to select the feature wavelengths ranging from 431 to 962 nm. Final results showed that BOSS can improve prediction performance and greatly reduce features when compared with the other two selection methods. The BOSS model got the best accuracy in calibration and prediction with R2c of 0.9907 and RMSEC of 0.4257mg/kg, R2p of 0.9821, and RMSEP of 0.6461 mg/kg. Hence, the method of hyperspectral technology combined with the BOSS feature selection is feasible for detecting the cadmium content of tomato leaves, which can potentially provide a new method and thought for cadmium content detection of other crops. Keywords: Feature selection, Hyperspectral image technology, Non-destructive analysis, Regression model, Tomato leaves.
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Liu, Hong, Tao Yu, Bingliang Hu, Xingsong Hou, Zhoufeng Zhang, Xiao Liu, Jiacheng Liu, et al. "UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring." Remote Sensing 13, no. 20 (October 12, 2021): 4069. http://dx.doi.org/10.3390/rs13204069.
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Unmanned aerial vehicle (UAV) hyperspectral remote sensing technologies have unique advantages in high-precision quantitative analysis of non-contact water surface source concentration. Improving the accuracy of non-point source detection is a difficult engineering problem. To facilitate water surface remote sensing, imaging, and spectral analysis activities, a UAV-based hyperspectral imaging remote sensing system was designed. Its prototype was built, and laboratory calibration and a joint air–ground water quality monitoring activity were performed. The hyperspectral imaging remote sensing system of UAV comprised a light and small UAV platform, spectral scanning hyperspectral imager, and data acquisition and control unit. The spectral principle of the hyperspectral imager is based on the new high-performance acousto-optic tunable (AOTF) technology. During laboratory calibration, the spectral calibration of the imaging spectrometer and image preprocessing in data acquisition were completed. In the UAV air–ground joint experiment, combined with the typical water bodies of the Yangtze River mainstream, the Three Gorges demonstration area, and the Poyang Lake demonstration area, the hyperspectral data cubes of the corresponding water areas were obtained, and geometric registration was completed. Thus, a large field-of-view mosaic and water radiation calibration were realized. A chlorophyl-a (Chl-a) sensor was used to test the actual water control points, and 11 traditional Chl-a sensitive spectrum selection algorithms were analyzed and compared. A random forest algorithm was used to establish a prediction model of water surface spectral reflectance and water quality parameter concentration. Compared with the back propagation neural network, partial least squares, and PSO-LSSVM algorithms, the accuracy of the RF algorithm in predicting Chl-a was significantly improved. The determination coefficient of the training samples was 0.84; root mean square error, 3.19 μg/L; and mean absolute percentage error, 5.46%. The established Chl-a inversion model was applied to UAV hyperspectral remote sensing images. The predicted Chl-a distribution agreed with the field observation results, indicating that the UAV-borne hyperspectral remote sensing water quality monitoring system based on AOTF is a promising remote sensing imaging spectral analysis tool for water.
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Musci, M. A., I. Aicardi, P. Dabove, and A. M. Lingua. "RELIABILITY OF THE GEOMETRIC CALIBRATION OF AN HYPERSPECTRAL FRAME CAMERA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1701–7. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1701-2019.
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<p><strong>Abstract.</strong> One of the main tools for high resolution remote sensing and photogrammetry is the lightweight hyperspectral frame camera, that is used in several application areas such as precision agriculture, forestry, and environmental monitoring. Among these types of sensors, the Rikola (which is based on a Fabry–Perot interferometer (FPI) and produced by Senop) is one of the latest innovations. Due to its internal geometry, there are several issues to be addressed for the appropriate definition and estimation of the inner orientation parameters (IOPs). The main problems concern the possibility to change every time the sequence of the bands and to assess the reliability of the IOPs. This work focuses the attention on the assessment of the IOPs definition for each sensor, considering the impact of environmental conditions (e.g., different time, exposure, brightness) and different configurations of the FPI camera, in order to rebuild an undistorted hypercube for image processing and object estimation. The aim of this work is to understand if the IOPs are stable over the time and if and which bands can be used as reference for the calculation of the inner parameters for each sensor, considering different environmental configurations and surveys, from terrestrial to aerial applications. Preliminary performed tests showed that the focal length percentage variation among the bands of different experiments is around 1%.</p>
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Gómez Manzanares, Ángela, Daniel Vázquez Moliní, Antonio Alvarez Fernandez-Balbuena, Santiago Mayorga Pinilla, and Juan Carlos Martínez Antón. "Measuring High Dynamic Range Spectral Reflectance of Artworks through an Image Capture Matrix Hyperspectral Camera." Sensors 22, no. 13 (June 21, 2022): 4664. http://dx.doi.org/10.3390/s22134664.
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Commercial hyperspectral imaging systems typically use CCD or CMOS sensors. These types of sensors have a limited dynamic range and non-linear response. This means that when evaluating an artwork under uncontrolled lighting conditions and with light and dark areas in the same scene, hyperspectral images with underexposed or saturated areas would be obtained at low or high exposure times, respectively. To overcome this problem, this article presents a system for capturing hyperspectral images consisting of a matrix of twelve spectral filters placed in twelve cameras, which, after processing these images, makes it possible to obtain the high dynamic range image to measure the spectral reflectance of the work of art being evaluated. We show the developed system and describe all its components, calibration processes, and the algorithm implemented to obtain the high dynamic range spectral reflectance measurement. In order to validate the system, high dynamic range spectral reflectance measurements from Labsphere’s Spectralon Reflectance Standards were performed and compared with the same reflectance measurements but using low dynamic range images. High dynamic range hyperspectral imaging improves the colorimetric accuracy and decreases the uncertainty of the spectral reflectance measurement based on low dynamic range imaging.
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Teng, Yidan, Ye Zhang, Chunli Ti, and Junping Zhang. "Hyperspectral Image Resolution Enhancement Approach Based on Local Adaptive Sparse Unmixing and Subpixel Calibration." Remote Sensing 10, no. 4 (April 11, 2018): 592. http://dx.doi.org/10.3390/rs10040592.
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Kim, Geonwoo, Insuck Baek, Matthew D. Stocker, Jaclyn E. Smith, Andrew L. Van Tassell, Jianwei Qin, Diane E. Chan, Yakov Pachepsky, and Moon S. Kim. "Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water." Remote Sensing 12, no. 13 (June 27, 2020): 2070. http://dx.doi.org/10.3390/rs12132070.
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This study provides detailed information about the use of a hyperspectral imaging system mounted on a motor-driven multipurpose floating platform (MFP) for water quality sensing and water sampling, including the spatial and spectral calibration for the camera, image acquisition and correction procedures. To evaluate chlorophyll-a concentrations in an irrigation pond, visible/near-infrared hyperspectral images of the water were acquired as the MFP traveled to ten water sampling locations along the length of the pond, and dimensionality reduction with correlation analysis was performed to relate the image data to the measured chlorophyll-a data. About 80,000 sample images were acquired by the line-scan method. Image processing was used to remove sun-glint areas present in the raw hyperspectral images before further analysis was conducted by principal component analysis (PCA) to extract three key wavelengths (662 nm, 702 nm, and 752 nm) for detecting chlorophyll-a in irrigation water. Spectral intensities at the key wavelengths were used as inputs to two near-infrared (NIR)-red models. The determination coefficients (R2) of the two models were found to be about 0.83 and 0.81. The results show that hyperspectral imagery from low heights can provide valuable information about water quality in a fresh water source.
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Brook, A., and E. Ben Dor. "Practical example for use of the supervised vicarious calibration (SVC) method on multisource hyperspectral imagery data – ValCalHyp airborne hyperspectral campaign under the EUFAR framework." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 51–53. http://dx.doi.org/10.5194/isprsarchives-xl-7-51-2014.
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A novel approach for radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data, termed supervised vicarious calibration (SVC) was proposed by Brook and Ben-Dor in 2010. The present study was aimed at validating this SVC approach by simultaneously using several different airborne HSR sensors that acquired HSR data over several selected sites at the same time. The general goal of this study was to apply a cross-calibration approach to examine the capability and stability of the SVC method and to examine its validity. This paper reports the result of the multi sensors campaign took place over Salon de Provenance, France on behalf of the ValCalHyp project took place in 2011. The SVC method enabled the rectification of the radiometric drift of each sensor and improves their performance significantly. The flight direction of the SVC targets was found to be a critical issue for such correction and recommendations have been set for future utilization of this novel method. The results of the SVC method were examined by comparing ground-truth spectra of several selected validation targets with the image spectra as well as by comparing the classified water quality images generated from all sensors over selected water bodies.
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Duan, Zhaolin, Hao Chen, Xiaohua Li, Jiliu Zhou, and Yuan Wang. "A Semi-Supervised Learning Method for Hyperspectral-Image Open Set Classification." Photogrammetric Engineering & Remote Sensing 88, no. 10 (October 1, 2022): 653–64. http://dx.doi.org/10.14358/pers.21-00067r3.
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We present a conceptually simple and flexible method for hyperspectral-image open set classification. Unlike previous methods, where the abundant unlabeled data inherent in the data set are ignored completely and unknown classes are inferred using score post-calibration, our approach makes the unlabeled data join in and help to train a simple and practical model for open set classification. The model is able to provide an explicit decision score for both unknown classes and each known class. The main idea of the proposed method is augmenting the original training set of K known classes using the pseudo-labeled unknown-category samples that are detected elaborately from the unlabeled data using modified OpenMax and semi-supervised iterative learning. Then a (K + 1)-class deep convolutional neural network model is trained based on the augmented training set with (K + 1) class samples. The model can not only classify instances of each known class but also refuse instances of unknown class explicitly. We validated the proposed method on four well-known hyperspectral-image data sets, obtaining superior performance over previous methods.
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Khan, Haris, Sofiane Mihoubi, Benjamin Mathon, Jean-Baptiste Thomas, and Jon Hardeberg. "HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images." Sensors 18, no. 7 (June 26, 2018): 2045. http://dx.doi.org/10.3390/s18072045.
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We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.
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Deshpande, ShaileshS, ArunB Inamdar, and HarrickM Vin. "Urban Land Use/Land Cover Discrimination Using Image-Based Reflectance Calibration Methods for Hyperspectral Data." Photogrammetric Engineering & Remote Sensing 83, no. 5 (May 1, 2017): 365–76. http://dx.doi.org/10.14358/pers.83.5.365.
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Li, Litao, Zhen Li, Zhixin Wang, Yonghua Jiang, Xin Shen, and Jiaqi Wu. "On-Orbit Relative Radiometric Calibration of the Bayer Pattern Push-Broom Sensor for Zhuhai-1 Video Satellites." Remote Sensing 15, no. 2 (January 7, 2023): 377. http://dx.doi.org/10.3390/rs15020377.
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The two video satellites of the second and third batch of Zhuhai-1 microsatellites (referred to as OVS-2A/3A) are operational with their hyperspectral satellites, which improves the data acquisi-tion capability of the Zhuhai-1 remote sensing satellite constellation. Contrary to the linear array push-broom hyperspectral satellites and plane array CCD video satellites, the OVS satellite is equipped with a planar array Bayer pattern sensor, which can obtain single-band grayscale images by push-broom imaging. Additionally, the Bayer color reconstruction algorithm can interpolate sensor data to provide RGB color band information. Therefore, for the Bayer pattern push-broom sensor, the relative calibration method of linear push-broom or array cameras cannot be directly applied. The radiometric calibration of the Bayer pattern push-broom imaging mode has become a matter of concern; therefore, this study developed a radiometric calibration method for the Bayer pattern push-broom sensor of the OVS satellite and verified its effectiveness and accuracy. OVS images were used to perform on-orbit relative radiometric calibration, and the calibration accu-racy, including streaking metrics and root-mean-square error, was better than 1%, meeting the specification requirements for the OVS satellite. Visually, after calibration correction, the streaking and striping noise of the Bayer images was removed, and the radiometric quality of the image was considerably improved, providing a good data basis for subsequent research in remote sensing applications.
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Ru, Chenlei, Zhenhao Li, and Renzhong Tang. "A Hyperspectral Imaging Approach for Classifying Geographical Origins of Rhizoma Atractylodis Macrocephalae Using the Fusion of Spectrum-Image in VNIR and SWIR Ranges (VNIR-SWIR-FuSI)." Sensors 19, no. 9 (May 1, 2019): 2045. http://dx.doi.org/10.3390/s19092045.
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Hyperspectral data processing technique has gained increasing interests in the field of chemical and biomedical analysis. However, appropriate approaches to fusing features of hyperspectral data-cube are still lacking. In this paper, a new data fusion approach was proposed and applied to discriminate Rhizoma Atractylodis Macrocephalae (RAM) slices from different geographical origins using hyperspectral imaging. Spectral and image features were extracted from hyperspectral data in visible and near-infrared (VNIR, 435–1042 nm) and short-wave infrared (SWIR, 898–1751 nm) ranges, respectively. Effective wavelengths were extracted from pre-processed spectral data by successive projection algorithm (SPA). Meanwhile, gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) were employed to extract textural variables. The fusion of spectrum-image in VNIR and SWIR ranges (VNIR-SWIR-FuSI) was implemented to integrate those features on three fusion dimensions, i.e., VNIR and SWIR fusion, spectrum and image fusion, and all data fusion. Based on data fusion, partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were utilized to establish calibration models. The results demonstrated that VNIR-SWIR-FuSI could achieve the best accuracies on both full bands (97.3%) and SPA bands (93.2%). In particular, VNIR-SWIR-FuSI on SPA bands achieved a classification accuracy of 93.2% with only 23 bands, which was significantly better than those based on spectra (80.9%) or images (79.7%). Thus it is more rapid and possible for industry applications. The current study demonstrated that hyperspectral imaging technique with data fusion holds the potential for rapid and nondestructive sorting of traditional Chinese medicines (TCMs).
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Vreys, Kristin, Marian-Daniel Iordache, Jan Biesemans, and Koen Meuleman. "Geometric correction of APEX hyperspectral data." Miscellanea Geographica 20, no. 1 (March 1, 2016): 11–15. http://dx.doi.org/10.1515/mgrsd-2016-0006.
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Abstract Hyperspectral imagery originating from airborne sensors is nowadays widely used for the detailed characterization of land surface. The correct mapping of the pixel positions to ground locations largely contributes to the success of the applications. Accurate geometric correction, also referred to as “orthorectification”, is thus an important prerequisite which must be performed prior to using airborne imagery for evaluations like change detection, or mapping or overlaying the imagery with existing data sets or maps. A so-called “ortho-image” provides an accurate representation of the earth’s surface, having been adjusted for lens distortions, camera tilt and topographic relief. In this paper, we describe the different steps in the geometric correction process of APEX hyperspectral data, as applied in the Central Data Processing Center (CDPC) at the Flemish Institute for Technological Research (VITO, Mol, Belgium). APEX ortho-images are generated through direct georeferencing of the raw images, thereby making use of sensor interior and exterior orientation data, boresight calibration data and elevation data. They can be referenced to any userspecified output projection system and can be resampled to any output pixel size.
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Tung, Kuo-Chih, Ping-Lang Yen, Chao-Yin Tsai, Pauline Ong, Jer-Wei Lin, Yung-Huei Chang, and Suming Chen. "Nondestructive Quantitative Analysis of Water Potential of Tomato Leaves Using Online Hyperspectral Imaging System." Applied Engineering in Agriculture 38, no. 2 (2022): 273–82. http://dx.doi.org/10.13031/aea.14800.
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HighlightsWe developed an online measurement system for water potential of tomato plants using hyperspectral imaging.We used Linear Discriminant Analysis to automatically and quickly extract the leaf images.We used SNV scattering correction to remove the spectral variations caused by collecting the defocused leaf images.We developed a prediction model of leaf water potential based on spectral image information.Abstract. Tomatoes have different water requirements in each growing period. Excessive water use or insufficient water supply will affect the growth and yield of tomato plants. Therefore, precise irrigation control is necessary during cultivation to increase crop productivity. Traditionally, the soil moisture content or leaf water potential has been used as an indicator of plant water status. These methods, however, have limited accuracy and are time-consuming, making it difficult to be put into practice in tomato production. This study developed an online hyperspectral imaging system to measure the leaf water potential of tomato nondestructively. Linear Discriminant Analysis was utilized to automatically and quickly extract the leaf images, with the recognition accuracy of 94.68% was achieved. The mathematical processing of Standard Normal Variate scattering correction was used to remove the spectral variations caused by the defocused leave images. The developed leaf water potential prediction model based on the spectral image information attained using the developed system achieved the standard error of calibration of 0.201, coefficient of determination in calibration set of 0.814 and standard error of cross-validation of 0.230, and one minus the variance ratio of 0.755. The obtained performance indicated the feasibility of applying the developed online hyperspectral imaging system as a real-time non-destructive measurement technique for the leaf water potential of tomato plants. Keywords: Hyperspectral imaging system, Machine learning, Tomato, Water potential.
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Liu, Bohan, Shaojie Men, Zhongjun Ding, Dewei Li, Zhigang Zhao, Jiahao He, Haochen Ju, Mengling Shen, Qiuyuan Yu, and Zhaojun Liu. "Underwater Hyperspectral Imaging System with Liquid Lenses." Remote Sensing 15, no. 3 (January 17, 2023): 544. http://dx.doi.org/10.3390/rs15030544.
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The underwater hyperspectral imager enables the detection and identification of targets on the seafloor by collecting high-resolution spectral images. The distance between the hyperspectral imager and the targets cannot be consistent in real operation by factors such as motion and fluctuating terrain, resulting in unfocused images and negative effects on the identification. In this paper, we developed a novel integrated underwater hyperspectral imaging system for deep sea surveys and proposed an autofocus strategy based on liquid lens focusing transfer. The calibration tests provided a clear focus result for hyperspectral transects and a global spectral resolution of less than 7 nm in spectral range from 400 to 800 nm. The prototype was used to obtain spectrum and image information of manganese nodules and four other rocks in a laboratory environment. The classification of the five kinds of minerals was successfully realized by using a support vector machine. We tested the UHI prototype in the deep sea and observed a Psychropotidae specimen on the sediment from the in situ hyperspectral images. The results show that the prototype developed here can accurately and stably obtain hyperspectral data and has potential applications for in situ deep-sea exploration.
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Fan, Yuhai, Yuiqing Wan, Hui Wang, Xingke Yang, Min Liang, Chunjuan Pan, Shaopeng Zhang, Wenbo Wang, and Furong Tan. "Application of an airborne hyper-spectral survey system CASI/SASI in the gold-silver-lead-zinc ore district of Huaniushan, Gansu, China." Geologia Croatica 74, no. 1 (February 28, 2021): 73–83. http://dx.doi.org/10.4154/gc.2021.04.
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The airborne hyper-spectral survey system CASI/SASI, which has an integrated system for gathering both image an spectral data, is at the cutting edge developments in the remote-sensing field. It can be used to directly identify surface objects based on diagnostic spectral characteristics. In this paper, the CASI/SASI were used in the Huaniushan gold-silver-lead-zinc ore district–Gansu to produce a lithologic map, identify altered minerals, and map the mineralized-alteration zones. Radiometric correction, radiometric calibration, atmospheric correction (spectral reconstruction), and geometric corrections were carried out in ENVI to pre-process the measured data. A FieldSpec ® Pro FR portable spectrometer was used to obtain the spectral signatures of all types of rock samples, ore deposits, and mineralized-alteration zones. We extracted and analyzed the spectral characteristics of typical alteration minerals. On the basis of hyper-spectral data, ground-spectral data processing, and comparative analysis of the measured image spectrum, we used the spectral-angle-mapping (SAM) and mixture-tuned matchedfiltering (MTMF) methods to perform hyperspectral-alteration mineral mapping of wall rock and mineralized-alteration-zone hyperspectral identification. Hyperspectral- remote- sensing geological- classification maps were produced as well as distribution maps of all kinds of alteration minerals and mineralized-alteration zones. Based on geological comprehensive analysis and field investigations, the range of mineral alteration was proven to be the same as shown by the remote-sensing imagery. Indications are that airborne hyperspectral- remote-sensing -image CASI/SASI offer good application results and show a promising potential as a tool in geological investigations. The results will provide the basis for hyperspectral remote-sensing prospecting in the same or similar unexplored areas.
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Poshekhonov, V., V. Eremeev, А. Kuznetcov, and A. Kochergin. "MODELS FOR PHOTOGRAMMETRIC PROCESSING OF INFORMATION FROM “RESOURCE-P” SATELLITES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B6 (June 17, 2016): 169–71. http://dx.doi.org/10.5194/isprs-archives-xli-b6-169-2016.
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The present paper provides information about imagery and navigation systems of the Russian high resolution satellites "Resource- P". Models of image geolocation used for photogrammetric processing of information from all types of imagery systems are designed. Design of these models is based on two task solutions: correct processing of the measurement information and geometric calibration of the imagery systems. <br><br> It is shown that for high-precision interior orientation parameters adjustment of the high-resolution "Geoton" instrument the method of self-calibration should be used. The technology of calibration activities is considered. Distinctive features of calibration of the hyperspectral and wide-swath imagery systems are noted. It is represented in the paper that after calibration the root mean square error (RMSE) of measured geodetic coordinates of objects on images do not exceed 10 m. <br><br> Examples of the obtained models practical application for photogrammetric processing of images from “Resource-P” satellites are shown.
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Rahkonen, Samuli, Leevi Lind, Anna-Maria Raita-Hakola, Sampsa Kiiskinen, and Ilkka Pölönen. "Reflectance Measurement Method Based on Sensor Fusion of Frame-Based Hyperspectral Imager and Time-of-Flight Depth Camera." Sensors 22, no. 22 (November 10, 2022): 8668. http://dx.doi.org/10.3390/s22228668.
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Hyperspectral imaging and distance data have previously been used in aerial, forestry, agricultural, and medical imaging applications. Extracting meaningful information from a combination of different imaging modalities is difficult, as the image sensor fusion requires knowing the optical properties of the sensors, selecting the right optics and finding the sensors’ mutual reference frame through calibration. In this research we demonstrate a method for fusing data from Fabry–Perot interferometer hyperspectral camera and a Kinect V2 time-of-flight depth sensing camera. We created an experimental application to demonstrate utilizing the depth augmented hyperspectral data to measure emission angle dependent reflectance from a multi-view inferred point cloud. We determined the intrinsic and extrinsic camera parameters through calibration, used global and local registration algorithms to combine point clouds from different viewpoints, created a dense point cloud and determined the angle dependent reflectances from it. The method could successfully combine the 3D point cloud data and hyperspectral data from different viewpoints of a reference colorchecker board. The point cloud registrations gained 0.29–0.36 fitness for inlier point correspondences and RMSE was approx. 2, which refers a quite reliable registration result. The RMSE of the measured reflectances between the front view and side views of the targets varied between 0.01 and 0.05 on average and the spectral angle between 1.5 and 3.2 degrees. The results suggest that changing emission angle has very small effect on the surface reflectance intensity and spectrum shapes, which was expected with the used colorchecker.
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Markelin, L., J. Suomalainen, T. Hakala, R. A. Oliveira, N. Viljanen, R. Näsi, B. Scott, et al. "METHODOLOGY FOR DIRECT REFLECTANCE MEASUREMENT FROM A DRONE: SYSTEM DESCRIPTION, RADIOMETRIC CALIBRATION AND LATEST RESULTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 283–88. http://dx.doi.org/10.5194/isprs-archives-xlii-1-283-2018.
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<p><strong>Abstract.</strong> We study and analyse performance of a system for direct reflectance measurements from a drone. Key instruments of the system are upwards looking irradiance sensor and downwards looking imaging spectrometer. Requirement for both instruments is that they are radiometrically calibrated, the irradiance sensor has to be horizontally stabilized, and the sensors needs to be accurately synchronized. In our system, irradiance measurements are done with <i>FGI Aerial Image Reference System</i> (FGI AIRS), which uses novel optical levelling methodology and can compensate sensor tilting up to 15&deg;. We performed SI-traceable spectral and radiance calibration of FPI hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). After the calibration, the radiance accuracy of different channels was between &plusmn;4<span class="thinspace"></span>% when evaluated with independent test data. Sensors response to radiance proved to be highly linear and was on average 0.9994 for all channels. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI and highlighted the importance of accurate calibration. The drone-based direct reflectance measurement system showed promising results with imagery collected over Jokioinen agricultural grass test site, Finland. AIRS-based image- and band wise image adjustment provided homogenous and seamless image mosaics even under varying illumination conditions and under clouds.</p>
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Jemec, Jurij, Franjo Pernuš, Boštjan Likar, and Miran Bürmen. "Push-broom hyperspectral image calibration and enhancement by 2D deconvolution with a variant response function estimate." Optics Express 22, no. 22 (October 31, 2014): 27655. http://dx.doi.org/10.1364/oe.22.027655.
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Katkovsky, Leonid, Anton Martinov, Volha Siliuk, Dimitry Ivanov, and Alexander Kokhanovsky. "Fast Atmospheric Correction Method for Hyperspectral Data." Remote Sensing 10, no. 11 (October 28, 2018): 1698. http://dx.doi.org/10.3390/rs10111698.
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Atmospheric correction is a necessary step in processing data recorded by spaceborne sensors for cloudless atmosphere, primarily in the visible and near-IR spectral range. In this paper we present a fast and sufficiently accurate method of atmospheric correction based on the analytical solutions of radiative transfer equation (RTE). The proposed analytical equations can be used to calculate the spectrum of outgoing radiation at the top boundary of the cloudless atmosphere. The solution of the inverse problem for finding unknown parameters of the model is carried out by the method of non-linear least squares (Levenberg-Marquardt algorithm) for an individual selected pixel of the image, taking into account the adjacency effects. Using the found parameters of the atmosphere and the average surface reflectance, and also assuming homogeneity of the atmosphere within a certain area of the hyperspectral image (or within the whole frame), the spectral reflectance at the Earth’s surface is calculated for all other pixels. It is essential that the procedure of the numerical simulation using non-linear least squares is based on the analytical solution of the direct transfer problem. This enables fast solution of the inverse problem in a very short calculation time. Testing of the method has been performed using the synthetic outgoing radiation spectra at the top of atmosphere, obtained from the LibRadTran code. In addition, we have used the spectra measured by the Hyperion. A comparison with the results of atmospheric correction in module FLAASH of ENVI package has been performed. Finally, to validate data obtained by our method, a comparative analysis with ground-based measurements of the Radiometric Calibration Network (RadCalNet) was carried out.
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Raut, Bipin, Morakot Kaewmanee, Amit Angal, Xiaoxiong Xiong, and Dennis Helder. "Empirical Absolute Calibration Model for Multiple Pseudo-Invariant Calibration Sites." Remote Sensing 11, no. 9 (May 9, 2019): 1105. http://dx.doi.org/10.3390/rs11091105.
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This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models at predicting sensor top-of-atmosphere (TOA) reflectance. It attempts to generate absolute calibration models for these PICS that have an accuracy and precision comparable to or better than the current Libya 4 model, with the intent of providing additional opportunities for sensor calibration. In addition, this work attempts to validate the general applicability of the model to other sites. The method uses Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as the reference radiometer and Earth Observing-1 (EO-1) Hyperion image data to provide a representative hyperspectral reflectance profile of the PICS. Data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the PICS are used for developing the model. The developed models were used to simulate observations of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Landsat 8 (L8) Operational Land Imager (OLI), Sentinel 2A (S2A) MultiSpectral Instrument (MSI) and Sentinel 2B (S2B) MultiSpectral Instrument (MSI) from their respective launch date through 2018. The models developed for the Egypt 1, Libya 1 and Sudan 1 PICS have an estimated accuracy of approximately 3% and precision of approximately 2% for the sensors used in the study, comparable to the current Libya 4 model. The models developed for the Niger 1 and Niger 2 sites are significantly less accurate with similar precision.
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Poshekhonov, V., V. Eremeev, А. Kuznetcov, and A. Kochergin. "MODELS FOR PHOTOGRAMMETRIC PROCESSING OF INFORMATION FROM “RESOURCE-P” SATELLITES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B6 (June 17, 2016): 169–71. http://dx.doi.org/10.5194/isprsarchives-xli-b6-169-2016.
Full textAbstract:
The present paper provides information about imagery and navigation systems of the Russian high resolution satellites "Resource- P". Models of image geolocation used for photogrammetric processing of information from all types of imagery systems are designed. Design of these models is based on two task solutions: correct processing of the measurement information and geometric calibration of the imagery systems. <br><br> It is shown that for high-precision interior orientation parameters adjustment of the high-resolution "Geoton" instrument the method of self-calibration should be used. The technology of calibration activities is considered. Distinctive features of calibration of the hyperspectral and wide-swath imagery systems are noted. It is represented in the paper that after calibration the root mean square error (RMSE) of measured geodetic coordinates of objects on images do not exceed 10 m. <br><br> Examples of the obtained models practical application for photogrammetric processing of images from “Resource-P” satellites are shown.
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Riu, Lucie, Cédric Pilorget, Vincent Hamm, Jean-Pierre Bibring, Cateline Lantz, Damien Loizeau, Rosario Brunetto, et al. "Calibration and performances of the MicrOmega instrument for the characterization of asteroid Ryugu returned samples." Review of Scientific Instruments 93, no. 5 (May 1, 2022): 054503. http://dx.doi.org/10.1063/5.0082456.
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MicrOmega, a miniaturized near-infrared hyperspectral microscope, has been selected to characterize in the laboratory the samples returned from Ryugu by the Hayabusa2 mission. MicrOmega has been delivered to the Extraterrestrial Samples Curation Center of the Japanese Aerospace eXploration Agency at the Institute of Space and Astronautical Science in July 2020 and then mounted and calibrated to be ready for the analyses of the samples returned to Earth on December 6, 2020. MicrOmega was designed to analyze the returned samples within a field of view of 5 × 5 mm2 and a spatial sampling of 22.5 µm. It acquires 3D near-infrared hyperspectral image-cubes by imaging the sample with monochromatic images sequentially covering the 0.99–3.65 µm spectral range, with a typical spectral sampling of 20 cm−1. This paper reports the calibration processes performed to extract scientific data from these MicrOmega image-cubes. The determination of the instrumental response and the spectral calibration is detailed. We meet or exceed the goals of achieving an accuracy of ∼20% for the absolute reflectance level, 1% for the relative wavelength-to-wavelength reflectance, and <5 nm for the peak position of the detected absorption features. For the nominal measurements of Ryugu samples with MicrOmega/Curation, the instrument performance also reaches a signal-to-noise ratio of >100 over the entire spectral range. By characterizing the entire collection of the returned samples at the microscopic scale, MicrOmega/Curation offers the potential to provide unprecedented insights into the composition and history of their asteroid parent body.
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Myronycheva, Olena, Ekaterina Sidorova, Olle Hagman, Margot Sehlstedt-Persson, Olov Karlsson, and Dick Sandberg. "Hyperspectral Imaging Surface Analysis for Dried and Thermally Modified Wood: An Exploratory Study." Journal of Spectroscopy 2018 (November 14, 2018): 1–10. http://dx.doi.org/10.1155/2018/7423501.
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Naturally seasoned, kiln-dried, and thermally modified wood has been studied by hyperspectral near-infrared imaging between 980 and 2500 nm in order to obtain spatial chemical information. Evince software was used to explore, preprocess, and analyse spectral data from image pixels and link these data to chemical information via spectral wavelength assignment. A PCA model showed that regions with high absorbance were related to extractives with phenolic groups and aliphatic hydrocarbons. The sharp wavelength band at 2135 nm was found by multivariate analysis to be useful for multivariate calibration. This peak represents the largest variation that characterizes the knot area and can be related to areas in wood rich in hydrocarbons and phenol, and it can perhaps be used for future calibration of other wood surfaces. The discriminant analysis of thermally treated wood showed the strongest differentiation between the planed and rip-cut wood surfaces and a fairly clear discrimination between the two thermal processes. The wavelength band at 2100 nm showed the greatest difference and may correspond to stretching of C=O-O of polymeric acetyl groups, but this requires confirmation by chemical analysis.
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Blanes, Ian, Aaron Kiely, Miguel Hernández-Cabronero, and Joan Serra-Sagristà. "Performance Impact of Parameter Tuning on the CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard." Remote Sensing 11, no. 11 (June 11, 2019): 1390. http://dx.doi.org/10.3390/rs11111390.
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This article studies the performance impact related to different parameter choices for the new CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression standard. This standard supersedes CCSDS-123.0-B-1 and extends it by incorporating a new near-lossless compression capability, as well as other new features. This article studies the coding performance impact of different choices for the principal parameters of the new extensions, in addition to reviewing related parameter choices for existing features. Experimental results include data from 16 different instruments with varying detector types, image dimensions, number of spectral bands, bit depth, level of noise, level of calibration, and other image characteristics. Guidelines are provided on how to adjust the parameters in relation to their coding performance impact.
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Markelin, L., E. Honkavaara, R. Näsi, K. Nurminen, and T. Hakala. "Geometric processing workflow for vertical and oblique hyperspectral frame images collected using UAV." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 205–10. http://dx.doi.org/10.5194/isprsarchives-xl-3-205-2014.
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Remote sensing based on unmanned airborne vehicles (UAVs) is a rapidly developing field of technology. UAVs enable accurate, flexible, low-cost and multiangular measurements of 3D geometric, radiometric, and temporal properties of land and vegetation using various sensors. In this paper we present a geometric processing chain for multiangular measurement system that is designed for measuring object directional reflectance characteristics in a wavelength range of 400–900 nm. The technique is based on a novel, lightweight spectral camera designed for UAV use. The multiangular measurement is conducted by collecting vertical and oblique area-format spectral images. End products of the geometric processing are image exterior orientations, 3D point clouds and digital surface models (DSM). This data is needed for the radiometric processing chain that produces reflectance image mosaics and multiangular bidirectional reflectance factor (BRF) observations. The geometric processing workflow consists of the following three steps: (1) determining approximate image orientations using Visual Structure from Motion (VisualSFM) software, (2) calculating improved orientations and sensor calibration using a method based on self-calibrating bundle block adjustment (standard photogrammetric software) (this step is optional), and finally (3) creating dense 3D point clouds and DSMs using Photogrammetric Surface Reconstruction from Imagery (SURE) software that is based on semi-global-matching algorithm and it is capable of providing a point density corresponding to the pixel size of the image. We have tested the geometric processing workflow over various targets, including test fields, agricultural fields, lakes and complex 3D structures like forests.
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Ilehag, R., A. Schenk, and S. Hinz. "CONCEPT FOR CLASSIFYING FACADE ELEMENTS BASED ON MATERIAL, GEOMETRY AND THERMAL RADIATION USING MULTIMODAL UAV REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 23, 2017): 145–51. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-145-2017.
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This paper presents a concept for classification of facade elements, based on the material and the geometry of the elements in addition to the thermal radiation of the facade with the usage of a multimodal Unmanned Aerial Vehicle (UAV) system. Once the concept is finalized and functional, the workflow can be used for energy demand estimations for buildings by exploiting existing methods for estimation of heat transfer coefficient and the transmitted heat loss. The multimodal system consists of a thermal, a hyperspectral and an optical sensor, which can be operational with a UAV. While dealing with sensors that operate in different spectra and have different technical specifications, such as the radiometric and the geometric resolution, the challenges that are faced are presented. Addressed are the different approaches of data fusion, such as image registration, generation of 3D models by performing image matching and the means for classification based on either the geometry of the object or the pixel values. As a first step towards realizing the concept, the result from a geometric calibration with a designed multimodal calibration pattern is presented.
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Storch, T., and R. Müller. "PROCESSING CHAINS FOR DESIS AND ENMAP IMAGING SPECTROSCOPY DATA: SIMILARITIES AND DIFFERENCES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W3 (October 20, 2017): 177–80. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w3-177-2017.
Full textAbstract:
The Earth Observation Center (EOC) of the German Aerospace Center (DLR) realizes operational processors for DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program) high-resolution imaging spectroscopy remote sensing satellite missions. DESIS is planned to be launched in 2018 and EnMAP in 2020. The developmental (namely schedule, deployment, and team) and functional (namely processing levels, algorithms in processors, and archiving approaches) similarities and differences of the fully-automatic processors are analyzed. The processing chains generate high-quality standardized image products for users at different levels taking characterization and calibration data into account. EOC has long lasting experiences with the airborne and spaceborne acquisition, processing, and analysis of hyperspectral image data. It turns out that both activities strongly benefit from each other.
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Zhang, Jinnuo, Xuping Feng, Xiaodan Liu, and Yong He. "Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology." Applied Sciences 8, no. 10 (October 1, 2018): 1793. http://dx.doi.org/10.3390/app8101793.
Full textAbstract:
Near-infrared (874–1734 nm) hyperspectral imaging technology combined with chemometrics was used to identify parental and hybrid okra seeds. A total of 1740 okra seeds of three different varieties, which contained the male parent xiaolusi, the female parent xianzhi, and the hybrid seed penzai, were collected, and all of the samples were randomly divided into the calibration set and the prediction set in a ratio of 2:1. Principal component analysis (PCA) was applied to explore the separability of different seeds based on the spectral characteristics of okra seeds. Fourteen and 86 characteristic wavelengths were extracted by using the successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. Another 14 characteristic wavelengths were extracted by using CARS combined with SPA. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were developed based on the characteristic wavelength and full-band spectroscopy. The experimental results showed that the SVM discriminant model worked well and that the correct recognition rate was over 93.62% based on full-band spectroscopy. As for the discriminative model that was based on characteristic wavelength, the SVM model based on the CARS algorithm was better than the other two models. Combining the CARS+SVM calibration model and image processing technology, a pseudo-color map of sample prediction was generated, which could intuitively identify the species of okra seeds. The whole process provided a new idea for agricultural breeding in the rapid screening and identification of hybrid okra seeds.
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Müller, R., J. Avbelj, E. Carmona, A. Eckardt, B. Gerasch, L. Graham, B. Günther, et al. "THE NEW HYPERSPECTRAL SENSOR DESIS ON THE MULTI-PAYLOAD PLATFORM MUSES INSTALLED ON THE ISS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 461–67. http://dx.doi.org/10.5194/isprsarchives-xli-b1-461-2016.
Full textAbstract:
The new hyperspectral instrument DLR Earth Sensing Imaging Spectrometer (DESIS) will be developed and integrated in the Multi-User-System for Earth Sensing (MUSES) platform installed on the International Space Station (ISS). The DESIS instrument will be launched to the ISS mid of 2017 and robotically installed in one of the four slots of the MUSES platform. After a four month commissioning phase the operational phase will last at least until 2020. The MUSES / DESIS system will be commanded and operated by the publically traded company TBE (Teledyne Brown Engineering), which initiated the whole program. TBE provides the MUSES platform and the German Aerospace Center (DLR) develops the instrument DESIS and establishes a Ground Segment for processing, archiving, delivering and calibration of the image data mainly used for scientific and humanitarian applications. Well calibrated and harmonized products will be generated together with the Ground Segment established at Teledyne. The article describes the Space Segment consisting of the MUSES platform and the instrument DESIS as well as the activities at the two (synchronized) Ground Segments consisting of the processing methods, product generation, data calibration and product validation. Finally comments to the data policy are given.
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Müller, R., J. Avbelj, E. Carmona, A. Eckardt, B. Gerasch, L. Graham, B. Günther, et al. "THE NEW HYPERSPECTRAL SENSOR DESIS ON THE MULTI-PAYLOAD PLATFORM MUSES INSTALLED ON THE ISS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 461–67. http://dx.doi.org/10.5194/isprs-archives-xli-b1-461-2016.
Full textAbstract:
The new hyperspectral instrument DLR Earth Sensing Imaging Spectrometer (DESIS) will be developed and integrated in the Multi-User-System for Earth Sensing (MUSES) platform installed on the International Space Station (ISS). The DESIS instrument will be launched to the ISS mid of 2017 and robotically installed in one of the four slots of the MUSES platform. After a four month commissioning phase the operational phase will last at least until 2020. The MUSES / DESIS system will be commanded and operated by the publically traded company TBE (Teledyne Brown Engineering), which initiated the whole program. TBE provides the MUSES platform and the German Aerospace Center (DLR) develops the instrument DESIS and establishes a Ground Segment for processing, archiving, delivering and calibration of the image data mainly used for scientific and humanitarian applications. Well calibrated and harmonized products will be generated together with the Ground Segment established at Teledyne. The article describes the Space Segment consisting of the MUSES platform and the instrument DESIS as well as the activities at the two (synchronized) Ground Segments consisting of the processing methods, product generation, data calibration and product validation. Finally comments to the data policy are given.
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Zhou, Quan, Wenqian Huang, and Xi Tian. "Feature Wavelength Selection Based on the Combination of Image and Spectrum for Aflatoxin B1 Concentration Classification in Single Maize Kernels." Agriculture 12, no. 3 (March 9, 2022): 385. http://dx.doi.org/10.3390/agriculture12030385.
Full textAbstract:
Aflatoxin B1 (AFB1) is a very strong carcinogen, maize kernels are easily infected by this toxin during storage. Rapid and accurate identification of AFB1 is of great significance to ensure food safety. In this study, a novel method for classification of AFB1 in single maize kernels was developed. Four groups of maize kernel samples with different AFB1 concentrations (10, 20, 50, and 100 ppb) were prepared by artificial inoculation of toxin. In addition, one group of maize kernel samples without AFB1 were prepared as control, each group with 70 samples. The visible and short wave near-infrared (Vis-SWNIR) region (500–1000 nm) and long wave near-infrared (LWNIR) region (1000–2000 nm) hyperspectral images of all samples were obtained respectively, and the hyperspectral images in 500–2000 nm range was obtained after spectral pretreatment and fusion. Kennard-Stone algorithm was used to divide the samples into calibration set or prediction set. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to roughly select the characteristic wavelengths of the calibration set samples, and 25 and 26 effective wavelengths were obtained respectively. Based on the roughly selected wavelengths, a method of fine selection of the characteristic wavelengths was proposed by using the gray-value difference of image (GDI), and a few number of characteristic wavelengths were further selected. Under the LDA classification model, 10 characteristic wavelengths were selected to test the prediction set and the independent verification samples, and the ideal result were obtained with an accuracy of 94.46% and 91.11%, respectively. This study provides a new approach for AFB1 concentration classification of single maize kernels.