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Journal articles on the topic 'Simulation multispectrale'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Han, Weimin, Wenxiang Cong, and Ge Wang. "Mathematical Study and Numerical Simulation of Multispectral Bioluminescence Tomography." International Journal of Biomedical Imaging 2006 (2006): 1–10. http://dx.doi.org/10.1155/ijbi/2006/54390.

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Multispectral bioluminescence tomography (BLT) attracts increasingly more attention in the area of optical molecular imaging. In this paper, we analyze the properties of the solutions to the regularized and discretized multispectral BLT problems. First, we show the solution existence, uniqueness, and its continuous dependence on the data. Then, we introduce stable numerical schemes and derive error estimates for numerical solutions. We report some numerical results to illustrate the performance of the numerical methods on the quality of multispectral BLT reconstruction.

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Cong, Alexander X., and Ge Wang. "Multispectral Bioluminescence Tomography: Methodology and Simulation." International Journal of Biomedical Imaging 2006 (2006): 1–7. http://dx.doi.org/10.1155/ijbi/2006/57614.

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Bioluminescent imaging has proven to be a valuable tool for monitoring physiological and pathological activities at cellular and molecular levels in living small animals. Using biological techniques, target cells can be tagged with reporters encoding several kinds of luciferase enzymes, which generate characteristic photons in a wide spectrum covering the infrared range. Part of the diffused light can reach the body surface of the small animal, be separated into several spectral bands using appropriate filters, and collected by a sensitive CCD camera. Here we present a bioluminescence tomography (BLT) method for a bioluminescent source reconstruction from multispectral data measured on the external surface, and demonstrate the advantages of multispectral BLT in a numerical study using a heterogeneous mouse chest phantom. The results show that the multispectral approach significantly improves the accuracy and stability of the BLT reconstruction even if the data are highly noisy.

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Clouet, Axel, Célia Viola, and Jérôme Vaillant. "Visible to near-infrared multispectral images dataset for image sensors design." Electronic Imaging 2020, no. 5 (2020): 106–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.5.maap-082.

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In this paper we present a set of multispectral images covering the visible and near-infrared spectral range (400 nm to 1050 nm). This dataset intends to provide spectral reflectance images containing daily life objects, usable for silicon image sensor simulations. All images were taken with our acquisition bench and a particular attention was brought to processings in order to provide calibrated reflectance data. ReDFISh (Reflectance Dataset For Image sensor Simulation) is available at: http://dx.doi.org/10.18709/perscido.2020.01.ds289.

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Yu, Jun, and Magnus Ekström. "Multispectral image classification using wavelets: a simulation study." Pattern Recognition 36, no. 4 (2003): 889–98. http://dx.doi.org/10.1016/s0031-3203(02)00125-5.

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5

Ding, Mengjia, Peter WT Yuen, Jonathan Piper, et al. "Design of a Tunable Snapshot Multispectral Imaging System through Ray Tracing Simulation." Journal of Imaging 5, no. 1 (2019): 9. http://dx.doi.org/10.3390/jimaging5010009.

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Research on snapshot multispectral imaging has been popular in the remote sensing community due to the high demands of video-rate remote sensing system for various applications. Existing snapshot multispectral imaging techniques are mainly of a fixed wavelength type, which limits their practical usefulness. This paper describes a tunable multispectral snapshot system by using a dual prism assembly as the dispersion element of the coded aperture snapshot spectral imagers (CASSI). Spectral tuning is achieved by adjusting the air gap displacement of the dual prism assembly. Typical spectral shifts of about 1 nm at 400 nm and 12 nm at 700 nm wavelength have been achieved in the present design when the air-gap of the dual prism is changed from 4.24 mm to 5.04 mm. The paper outlines the optical designs, the performance, and the pros and cons of the dual-prism CASSI (DP-CASSI) system. The performance of the system is illustrated by TraceProTM ray tracing, to allow researchers in the field to repeat or to validate the results presented in this paper.

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Li, Xuelong, Yue Yuan, and Qi Wang. "Hyperspectral and Multispectral Image Fusion Based on Band Simulation." IEEE Geoscience and Remote Sensing Letters 17, no. 3 (2020): 479–83. http://dx.doi.org/10.1109/lgrs.2019.2926308.

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Siok, Katarzyna, Ireneusz Ewiak, and Agnieszka Jenerowicz. "Multi-Sensor Fusion: A Simulation Approach to Pansharpening Aerial and Satellite Images." Sensors 20, no. 24 (2020): 7100. http://dx.doi.org/10.3390/s20247100.

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The growing demand for high-quality imaging data and the current technological limitations of imaging sensors require the development of techniques that combine data from different platforms in order to obtain comprehensive products for detailed studies of the environment. To meet the needs of modern remote sensing, the authors present an innovative methodology of combining multispectral aerial and satellite imagery. The methodology is based on the simulation of a new spectral band with a high spatial resolution which, when used in the pansharpening process, yields an enhanced image with a higher spectral quality compared to the original panchromatic band. This is important because spectral quality determines the further processing of the image, including segmentation and classification. The article presents a methodology of simulating new high-spatial-resolution images taking into account the spectral characteristics of the photographed types of land cover. The article focuses on natural objects such as forests, meadows, or bare soils. Aerial panchromatic and multispectral images acquired with a digital mapping camera (DMC) II 230 and satellite multispectral images acquired with the S2A sensor of the Sentinel-2 satellite were used in the study. Cloudless data with a minimal time shift were obtained. Spectral quality analysis of the generated enhanced images was performed using a method known as “consistency” or “Wald’s protocol first property”. The resulting spectral quality values clearly indicate less spectral distortion of the images enhanced by the new methodology compared to using a traditional approach to the pansharpening process.

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Kalukin, Andrew, Satoshi Endo, Russell Crook, et al. "Image Collection Simulation Using High-Resolution Atmospheric Modeling." Remote Sensing 12, no. 19 (2020): 3214. http://dx.doi.org/10.3390/rs12193214.

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A new method is described for simulating the passive remote sensing image collection of ground targets that includes effects from atmospheric physics and dynamics at fine spatial and temporal scales. The innovation in this research is the process of combining a high-resolution weather model with image collection simulation to attempt to account for heterogeneous and high-resolution atmospheric effects on image products. The atmosphere was modeled on a 3D voxel grid by a Large-Eddy Simulation (LES) driven by forcing data constrained by local ground-based and air-based observations. The spatial scale of the atmospheric model (10–100 m) came closer than conventional weather forecast scales (10–100 km) to approaching the scale of typical commercial multispectral imagery (2 m). This approach was demonstrated through a ground truth experiment conducted at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. In this experiment, calibrated targets (colored spectral tarps) were placed on the ground, and the scene was imaged with WorldView-3 multispectral imagery at a resolution enabling the tarps to be visible in at least 9–12 image pixels. The image collection was simulated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, using the 3D atmosphere from the LES model to generate a high-resolution cloud mask. The high-resolution atmospheric model-predicted cloud coverage was usually within 23% of the measured cloud cover. The simulated image products were comparable to the WorldView-3 satellite imagery in terms of the variations of cloud distributions and spectral properties of the ground targets in clear-sky regions, suggesting the potential utility of the proposed modeling framework in improving simulation capabilities, as well as testing and improving the operation of image collection processes.

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Sun, Bangyong, and Congjun Cao. "Simulation of proposed eight-band camera for capturing multispectral images." Multimedia Tools and Applications 77, no. 8 (2017): 10157–69. http://dx.doi.org/10.1007/s11042-017-5177-9.

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10

Il'inskiĭ, A. V., and N. K. Mal'tseva. "Method of multispectral simulation of the radiation of point objects." Journal of Optical Technology 77, no. 2 (2010): 137. http://dx.doi.org/10.1364/jot.77.000137.

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Han, Sanghui, and John P. Kerekes. "Overview of Passive Optical Multispectral and Hyperspectral Image Simulation Techniques." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 11 (2017): 4794–804. http://dx.doi.org/10.1109/jstars.2017.2759240.

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Devi, M. B., and R. Devanathan. "DATA FUSION OF PANCHROMATIC AND MULTISPECTRAL IMAGES BASED ON OPTIMIZATION USING LAGRANGE MULTIPLIER." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5 (November 15, 2018): 397–404. http://dx.doi.org/10.5194/isprs-annals-iv-5-397-2018.

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<p><strong>Abstract.</strong> The remote sensing satellites provide complementary images of different resolutions which need to be integrated using the techniques of image fusion. In this paper, image fusion using the IKONOS satellite data is discussed. Unlike other models which are based on sensor model, our approach is data centric including the effects of the sensor as well as the reflectance characteristics of the imaged object. A linear relationship is built between the panchromatic channel and the multispectral channel data. We then formulate a minimisation function in terms of Lagrange multiplier to optimally maximise the spectral consistency and minimise the error in variance. The variances of the downsampled multispectral channels are observed and compared with the original multispectral data. A chi-square goodness of fit test is performed to evaluate the data computed based on our algorithm. Simulation results are presented using the IKONOS 1m resolution panchromatic and 4<span class="thinspace"></span>m resolution multispectral data.</p>

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Siok, Katarzyna, and Ireneusz Ewiak. "The simulation approach to the interpretation of archival aerial photographs." Open Geosciences 12, no. 1 (2020): 1–10. http://dx.doi.org/10.1515/geo-2020-0001.

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AbstractArchival aerial photographs obtained in the 20th century play a special role in the process of upgrading the register of land and buildings. These photographs are the only resource presenting credible information on the coverage and use of land in a high degree of detail. In this article, particular attention is paid to the aspect of the spectral resolution of archival aerial photographs. Preliminary research was conducted into the assessment of the impact of simulations of new spectral bands with high spatial resolution for archival photographs upon the interpretation process of such data. The proposed simulation method of new bands is based on the integration of archival monochromatic aerial images with archival multispectral satellite imagery. Visual and quantitative comparative analysis of monochromatic imagery and of enhanced images obtained by application of the simulation process was performed. The results of this research unanimously confirmed the improved interpretation possibilities of archival aerial photographs, associated not only with the assignment of colours but also with the reconstruction of spectral information for the arable and afforested land photographed.

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14

Jung, Myunghee, and Melba M. Crawford. "Model based simulation of multispectral images based on remotely sensed data." Simulation Modelling Practice and Theory 11, no. 2 (2003): 151–69. http://dx.doi.org/10.1016/s1569-190x(03)00012-1.

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15

He, Xiaoyu, and Xiaojian Xu. "Physically Based Model for Multispectral Image Simulation of Earth Observation Sensors." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 5 (2017): 1897–908. http://dx.doi.org/10.1109/jstars.2017.2658678.

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16

Siok, Katarzyna, Agnieszka Jenerowicz, and Ireneusz Ewiak. "A simulation approach to the spectral quality of multispectral images enhancement." Computers and Electronics in Agriculture 174 (July 2020): 105432. http://dx.doi.org/10.1016/j.compag.2020.105432.

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17

Nardino, Vanni, Gabriele Amato, Donatella Guzzi, Cinzia Lastri, and Valentina Raimondi. "Experimental Tests on TIR Multispectral Images for Temperature-Emissivity Separation by Using the MaxEnTES Algorithm." Proceedings 27, no. 1 (2019): 10. http://dx.doi.org/10.3390/proceedings2019027010.

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Satellite images in the TIR are relevant for several Earth Observation applications. The retrieval of temperature and emissivity from the emitted radiance, however, requires the use of suitable algorithms, such as the MaxEnTES that uses the concept of maximum entropy to solve the Temperature-Emissivity Separation problem. Here we discuss the performance of MaxEnTES when applied to TIR images with a limited number of channels, specifically simulated HyspIRI multispectral images and real multispectral images by ASTER. The results were respectively compared with the original temperatures used for the simulations and with the temperatures obtained by using the ASTER TES algorithm.

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18

Kobayashi, T., E. Kanda, K. Kitada, K. Ishiguro, and Y. Torigoe. "Detection of Rice Panicle Blast with Multispectral Radiometer and the Potential of Using Airborne Multispectral Scanners." Phytopathology® 91, no. 3 (2001): 316–23. http://dx.doi.org/10.1094/phyto.2001.91.3.316.

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Rice reflectance was measured to determine the spectral regions most sensitive to panicle blast infection. Reflectance increased in the 430- to 530-, 580- to 680-, and 1,480- to 2,000-nm regions at the dough stage both in the laboratory and the field as the percentage of diseased spikelets increased. The wavebands of the greatest sensitivity were in the visible region, located near 485 and 675 nm. After the yellow-ripe growth stage, near-infrared rather than visible reflectance responded to panicle blast infections. Ratios of rice reflectance were evaluated as indicators of panicle blast. R470/R570 (reflectance at 470 nm divided by reflectance at 570 nm), R520/R675, and R570/R675 decreased significantly as the incidence of panicle blast increased at the dough stage. At the yellow-ripe stage, R550/R970 and R725/R900 were used to estimate panicle blast severity as measured in terms of the percentage of diseased spikelets. According to the simulation that uses ground-based sensor data, airborne multispectral scanners may be effective in detecting the occurrence of panicle blast using a band combination of 530- to 570- and 650- to 700-nm regions at the dough stage.

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19

Liu, Shenzhou, Wenzhi Zeng, Lifeng Wu, et al. "Simulating the Leaf Area Index of Rice from Multispectral Images." Remote Sensing 13, no. 18 (2021): 3663. http://dx.doi.org/10.3390/rs13183663.

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Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

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Iwabuchi, Hironobu, and Rintaro Okamura. "Multispectral Monte Carlo radiative transfer simulation by the maximum cross-section method." Journal of Quantitative Spectroscopy and Radiative Transfer 193 (May 2017): 40–46. http://dx.doi.org/10.1016/j.jqsrt.2017.01.025.

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Yaguchi, Hirohisa, Junyan Luo, Miharu Kato, and Yoko Mizokami. "Computerized simulation of color appearance for anomalous trichromats using the multispectral image." Journal of the Optical Society of America A 35, no. 4 (2018): B278. http://dx.doi.org/10.1364/josaa.35.00b278.

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Brydegaard, M., and S. Svanberg. "Simulation of multispectral x-ray imaging scenarios by Wien shift optical spectroscopy." American Journal of Physics 78, no. 2 (2010): 170–75. http://dx.doi.org/10.1119/1.3248356.

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23

Isaacs, R. G., and A. M. Vogelmann. "Multispectral sensor data simulation modeling based on the multiple scattering LOWTRAN code." Remote Sensing of Environment 26, no. 1 (1988): 75–99. http://dx.doi.org/10.1016/0034-4257(88)90120-4.

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Chen, Feng, Chenxing Wang, Yuansheng Zhang, et al. "Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation." Remote Sensing 13, no. 7 (2021): 1383. http://dx.doi.org/10.3390/rs13071383.

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Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among all Landsat sensors available currently, including Multispectral Scanner (MSS), Thematic Mappers (TM), Enhanced Thematic Mappers (ETM+), and Operational Land Imager (OLI)) in land cover mapping, based on a collection of synthesized, multispectral data. Compared to TM, OLI showed obvious between-sensor differences in channel reflectance, especially over the near infrared (NIR) and shortwave infrared (SWIR) channels, and presented positive bias in vegetation spectral indices. OLI did not always outperform TM and ETM+ in classification, which related to the methods used. Furthermore, the channels over SWIR of TM and its successors contributed largely to enhancement of inter-class separability and to improvement of classification. Currently, the inclusion of MSS data is confronted with significant challenges regarding the consistency of surface mapping. Considering the inconsistency among the Landsat sensors, it is applicable to generate a consistent time series of spectral indices through proper transformation models. Meanwhile, it suggests the generation of specific class(es) based on interest instead of including all classes simultaneously.

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SHANKAR, B. UMA, SAROJ K. MEHER, and ASHISH GHOSH. "NEURO-WAVELET CLASSIFIER FOR MULTISPECTRAL REMOTE SENSING IMAGES." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 04 (2007): 589–611. http://dx.doi.org/10.1142/s0219691307001914.

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A neuro-wavelet supervised classifier is proposed for land cover classification of multispectral remote sensing images. Features extracted from the original pixels information using wavelet transform (WT) are fed as input to a feed forward multi-layer neural network (MLP). The WT basically provides the spatial and spectral features of a pixel along with its neighbors and these features are used for improved classification. For testing the performance of the proposed method, we have used two IRS-1A satellite images and one SPOT satellite image. Results are compared with those of the original spectral feature based classifiers and found to be consistently better. Simulation study revealed that Biorthogonal 3.3 (Bior3.3) wavelet in combination with MLP performed better compared to all other wavelets. Results are evaluated visually and quantitatively with two measurements, β index of homogeneity and Davies–Bouldin (DB) index for compactness and separability of classes. We suggested a modified β index in accessing the percentage of accuracy (PAβ) of the classified images also.

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Farlik, Jan, Miroslav Kratky, Josef Casar, and Vadim Stary. "Multispectral Detection of Commercial Unmanned Aerial Vehicles." Sensors 19, no. 7 (2019): 1517. http://dx.doi.org/10.3390/s19071517.

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The fight against unmanned vehicles is nothing new; however, especially with the arrival of new technologies that are easily accessible for the wider population, new problems are arising. The deployment of small unmanned aerial vehicles (UAVs) by paramilitary organizations during conflicts around the world has become a reality, non-lethal “paparazzi” actions have become a common practice, and it is only a matter of time until the population faces lethal attacks. The basic prerequisite for direct defense against attacking UAVs is their detection. The authors of this paper analysed the possibility of detecting flying aircraft in several different electro-magnetic spectrum bands. Firstly, methods based on calculations and simulations were chosen, and experiments in laboratories and measurements of the exterior were subsequently performed. As a result, values of the radar cross section (RCS), the noise level, the surface temperature, and optical as well as acoustic traces of tested devices were quantified. The outputs obtained from calculated, simulated, and experimentally detected values were found via UAV detection distances using specific sensors working in corresponding parts of the frequency spectrum.

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Gao, Jianhao, Jie Li, and Menghui Jiang. "Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner." Remote Sensing 13, no. 16 (2021): 3226. http://dx.doi.org/10.3390/rs13163226.

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Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral images. An intelligent idea to obtain high-resolution hyperspectral images is hyperspectral and multispectral image fusion. In recent years, many studies have found that deep learning-based fusion methods outperform the traditional fusion methods due to the strong non-linear fitting ability of convolution neural network. However, the function of deep learning-based methods heavily depends on the size and quality of training dataset, constraining the application of deep learning under the situation where training dataset is not available or of low quality. In this paper, we introduce a novel fusion method, which operates in a self-supervised manner, to the task of hyperspectral and multispectral image fusion without training datasets. Our method proposes two constraints constructed by low-resolution hyperspectral images and fake high-resolution hyperspectral images obtained from a simple diffusion method. Several simulation and real-data experiments are conducted with several popular remote sensing hyperspectral data under the condition where training datasets are unavailable. Quantitative and qualitative results indicate that the proposed method outperforms those traditional methods by a large extent.

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Yagchi, Hirohisa, Junyan Luo, Miharu Kato, and Yoko Mizokami. "Computerized simulation of color appearance for anomalous trichromats using the multispectral image: erratum." Journal of the Optical Society of America A 37, no. 7 (2020): 1075. http://dx.doi.org/10.1364/josaa.396158.

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Chaudhari, Abhijit J., Sangtae Ahn, Richard Levenson, Ramsey D. Badawi, Simon R. Cherry, and Richard M. Leahy. "Excitation spectroscopy in multispectral optical fluorescence tomography: methodology, feasibility and computer simulation studies." Physics in Medicine and Biology 54, no. 15 (2009): 4687–704. http://dx.doi.org/10.1088/0031-9155/54/15/004.

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Tokutake, H., and M. Kido. "Localisation method for a Mars aircraft using multispectral images." Aeronautical Journal 122, no. 1253 (2018): 1083–101. http://dx.doi.org/10.1017/aer.2018.51.

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ABSTRACTIn Japan, the working group for the Mars Exploration Aircraft continues to research and develop a Mars aircraft aiming to a future survey mission. To ensure the success of the flight mission, a self-localisation system with low-computational complexity is necessary. In the present research, a new self-localisation method is proposed using multispectral images. The algorithm is based on the simple mapping of image moment invariants and gradients calculated from several images at different wavelengths. The numerical simulations revealed the sufficient robustness of the proposed method to image noise. The estimation accuracy can be improved by increasing the number of the spectral images.

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Grobler, T. L., E. R. Ackermann, A. J. van Zyl, J. C. Olivier, W. Kleynhans, and B. P. Salmon. "An Inductive Approach to Simulating Multispectral MODIS Surface Reflectance Time Series." IEEE Geoscience and Remote Sensing Letters 10, no. 3 (2013): 446–50. http://dx.doi.org/10.1109/lgrs.2012.2208446.

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32

Tominaga, Shoji. "Estimation Method of Fluorescent Donaldson Matrices Based on Multispectral Imaging Data." Color and Imaging Conference 2020, no. 28 (2020): 227–31. http://dx.doi.org/10.2352/issn.2169-2629.2020.28.36.

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This paper proposes a compact and reliable method to estimate the bispectral Donaldson matrices of fluorescent objects by using multispectral imaging data. We suppose that an image acquisition system allows multiple illuminant projections to the object surface and multiple response channels in the visible range. The Donaldson matrix is modeled as a twodimensional array with the excitation range (350, 700 nm) and the reflection and emission ranges (400, 700 nm). The observation model is described using the spectral sensitivities of a camera and the spectral functions of reflectance, emission, and excitation. The problem of estimating the spectral functions is formulated as a least squares problem to minimize the residual error of the observations and the roughness of the spectral functions. An iterative algorithm is developed to obtain the optimal estimates of the whole spectral functions. The performance of the proposed method is examined in simulation experiments using multispectral imaging data in detail.

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Sulub, Yusuf, and Gary W. Small. "Simulated Radiance Profiles for Automating the Interpretation of Airborne Passive Multi-Spectral Infrared Images." Applied Spectroscopy 62, no. 10 (2008): 1049–59. http://dx.doi.org/10.1366/000370208786049150.

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Methodology is developed for simulating the radiance profiles acquired from airborne passive multispectral infrared imaging measurements of ground sources of volatile organic compounds (VOCs). The simulation model allows the superposition of pure-component laboratory spectra of VOCs onto spectral backgrounds that simulate those acquired during field measurements conducted with a downward-looking infrared line scanner mounted on an aircraft flying at an altitude of 2000–3000 ft (approximately 600–900 m). Wavelength selectivity in the line scanner is accomplished through the use of a multichannel Hg:Cd:Te detector with up to 16 integrated optical filters. These filters allow the detection of absorption and emission signatures of VOCs superimposed on the upwelling infrared background radiance within the instrumental field of view (FOV). By combining simulated radiance profiles containing analyte signatures with field-collected background signatures, supervised pattern recognition methods can be employed to train automated classifiers for use in detecting the signatures of VOCs during field measurements. The targeted application for this methodology is the use of the imaging system to detect releases of VOCs during emergency response scenarios. In the work described here, the simulation model is combined with piecewise linear discriminant analysis to build automated classifiers for detecting ethanol and methanol. Field data collected during controlled releases of ethanol, as well as during a methanol release from an industrial facility, are used to evaluate the methodology.

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Hoang, Nguyen Tien, and Katsuaki Koike. "HYPERSPECTRAL TRANSFORMATION FROM EO-1 ALI IMAGERY USING PSEUDO-HYPERSPECTRAL IMAGE SYNTHESIS ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 661–65. http://dx.doi.org/10.5194/isprsarchives-xli-b7-661-2016.

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Hyperspectral remote sensing is more effective than multispectral remote sensing in many application fields because of having hundreds of observation bands with high spectral resolution. However, hyperspectral remote sensing resources are limited both in temporal and spatial coverage. Therefore, simulation of hyperspectral imagery from multispectral imagery with a small number of bands must be one of innovative topics. Based on this background, we have recently developed a method, Pseudo-Hyperspectral Image Synthesis Algorithm (PHISA), to transform Landsat imagery into hyperspectral imagery using the correlation of reflectance at the corresponding bands between Landsat and EO-1 Hyperion data. This study extends PHISA to simulate pseudo-hyperspectral imagery from EO-1 ALI imagery. The pseudo-hyperspectral imagery has the same number of bands as that of high-quality Hyperion bands and the same swath width as ALI scene. The hyperspectral reflectance data simulated from the ALI data show stronger correlation with the original Hyperion data than the one simulated from Landsat data. This high correlation originates from the concurrent observation by the ALI and Hyperion sensors that are on-board the same satellite. The accuracy of simulation results are verified by a statistical analysis and a surface mineral mapping. With a combination of the advantages of both ALI and Hyperion image types, the pseudo-hyperspectral imagery is proved to be useful for detailed identification of minerals for the areas outside the Hyperion coverage.

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Hoang, Nguyen Tien, and Katsuaki Koike. "HYPERSPECTRAL TRANSFORMATION FROM EO-1 ALI IMAGERY USING PSEUDO-HYPERSPECTRAL IMAGE SYNTHESIS ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 661–65. http://dx.doi.org/10.5194/isprs-archives-xli-b7-661-2016.

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Hyperspectral remote sensing is more effective than multispectral remote sensing in many application fields because of having hundreds of observation bands with high spectral resolution. However, hyperspectral remote sensing resources are limited both in temporal and spatial coverage. Therefore, simulation of hyperspectral imagery from multispectral imagery with a small number of bands must be one of innovative topics. Based on this background, we have recently developed a method, Pseudo-Hyperspectral Image Synthesis Algorithm (PHISA), to transform Landsat imagery into hyperspectral imagery using the correlation of reflectance at the corresponding bands between Landsat and EO-1 Hyperion data. This study extends PHISA to simulate pseudo-hyperspectral imagery from EO-1 ALI imagery. The pseudo-hyperspectral imagery has the same number of bands as that of high-quality Hyperion bands and the same swath width as ALI scene. The hyperspectral reflectance data simulated from the ALI data show stronger correlation with the original Hyperion data than the one simulated from Landsat data. This high correlation originates from the concurrent observation by the ALI and Hyperion sensors that are on-board the same satellite. The accuracy of simulation results are verified by a statistical analysis and a surface mineral mapping. With a combination of the advantages of both ALI and Hyperion image types, the pseudo-hyperspectral imagery is proved to be useful for detailed identification of minerals for the areas outside the Hyperion coverage.

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Benedetto, John J., Wojciech Czaja, and Martin Ehler. "Wavelet packets for time-frequency analysis of multispectral imagery." GEM - International Journal on Geomathematics 4, no. 2 (2013): 137–54. http://dx.doi.org/10.1007/s13137-013-0052-y.

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Chen Shan-Jing, Hu Yi-Hua, Sun Du-Juan, and Xu Shi-Long. "A simulation method by air and space integrated fusion based on hyper-/multispectral imagery." Acta Physica Sinica 62, no. 20 (2013): 204201. http://dx.doi.org/10.7498/aps.62.204201.

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38

Louargant, M., S. Villette, G. Jones, N. Vigneau, J. N. Paoli, and C. Gée. "Weed detection by UAV: simulation of the impact of spectral mixing in multispectral images." Precision Agriculture 18, no. 6 (2017): 932–51. http://dx.doi.org/10.1007/s11119-017-9528-3.

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Cuesta, J., M. Eremenko, X. Liu, et al. "Satellite observation of lowermost tropospheric ozone by multispectral synergism of IASI thermal infrared and GOME-2 ultraviolet measurements." Atmospheric Chemistry and Physics Discussions 13, no. 1 (2013): 2955–95. http://dx.doi.org/10.5194/acpd-13-2955-2013.

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Abstract. We present a new multispectral approach for observing lowermost tropospheric ozone from space by synergism of atmospheric radiances in the thermal infrared (TIR) observed by IASI and earth reflectances in the ultraviolet (UV) measured by GOME-2. Both instruments are onboard the series of MetOp satellites (in orbit since 2006 and expected until 2022) and their scanning capabilities offer global coverage every day, with a relatively fine ground pixel resolution (12-km-diameter pixels spaced by 25 km for IASI at nadir). Our technique uses altitude-dependent Tikhonov-Phillips-type constraints, which optimize sensitivity to lower tropospheric ozone. It integrates the VLIDORT and KOPRA radiative transfer codes for simulating UV reflectance and TIR radiance, respectively. We have used our method to analyse real observations over Europe during an ozone pollution episode in the summer of 2009. The results show that the multispectral synergism of IASI (TIR) and GOME-2 (UV) enables the observation of the spatial distribution of ozone plumes in the lowermost troposphere (LMT, from the surface up to 3 km a.s.l., above sea level), in good quantitative agreement with the CHIMERE regional chemistry-transport model. When high ozone concentrations extend vertically above 3 km a.s.l., they are similarly observed over land by both the multispectral and IASI retrievals. On the other hand, ozone plumes located below 3 km a.s.l. are only clearly depicted by the multispectral retrieval (both over land and over ocean). This is achieved by a clear enhancement of sensitivity to ozone in the lowest atmospheric layers. The multispectral sensitivity in the LMT peaks at 2 to 2.5 km a.s.l. over land, while sensitivity for IASI or GOME-2 only peaks at 3 to 4 km a.s.l. at lowest (above the LMT). The degrees of freedom for the multispectral retrieval increase by 40% (21%) with respect to IASI only retrievals for atmospheric partial columns up to 3 km a.s.l. (6 km a.s.l.). Validations with ozonesondes show that our synergetic approach for combining IASI (TIR) and GOME-2 (UV) measurements retrieves lowermost tropospheric ozone with a mean bias of 2% and a precision of 16%, when smoothing by the retrieval vertical sensitivity (1% mean bias and 24% precision for direct comparisons).

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Lionello, Roberto, Jon A. Linker, and Zoran Mikić. "MULTISPECTRAL EMISSION OF THE SUN DURING THE FIRST WHOLE SUN MONTH: MAGNETOHYDRODYNAMIC SIMULATIONS." Astrophysical Journal 690, no. 1 (2008): 902–12. http://dx.doi.org/10.1088/0004-637x/690/1/902.

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Badola, Anushree, Santosh K. Panda, Dar A. Roberts, et al. "Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska." Remote Sensing 13, no. 9 (2021): 1693. http://dx.doi.org/10.3390/rs13091693.

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Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

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Yuan, Zhen, and Huabei Jiang. "Quantitative photoacoustic tomography." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1900 (2009): 3043–54. http://dx.doi.org/10.1098/rsta.2009.0083.

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In this paper, several algorithms that allow for quantitative photoacoustic reconstruction of tissue optical, acoustic and physiological properties are described in a finite-element method based framework. These quantitative reconstruction algorithms are compared, and the merits and limitations associated with these methods are discussed. In addition, a multispectral approach is presented for concurrent reconstructions of multiple parameters including deoxyhaemoglobin, oxyhaemoglobin and water concentrations as well as acoustic speed. Simulation and in vivo experiments are used to demonstrate the effectiveness of the reconstruction algorithms presented.

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Huang, Zehua, Qi Chen, Qihao Chen, Xiuguo Liu, and Hao He. "A Novel Hyperspectral Image Simulation Method Based on Nonnegative Matrix Factorization." Remote Sensing 11, no. 20 (2019): 2416. http://dx.doi.org/10.3390/rs11202416.

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Hyperspectral (HS) images can provide abundant and fine spectral information on land surface. However, their applications may be limited by their narrow bandwidth and small coverage area. In this paper, we propose an HS image simulation method based on nonnegative matrix factorization (NMF), which aims at generating HS images using existing multispectral (MS) data. Our main novelty is proposing a spectral transformation matrix and new simulation method. First, we develop a spectral transformation matrix that transforms HS endmembers into MS endmembers. Second, we utilize an iteration scheme to optimize the HS and MS endmembers. The test MS image is then factorized by the MS endmembers to obtain the abundance matrix. The result image is constructed by multiplying the abundance matrix by the HS endmembers. Experiments prove that our method provides high spectral quality by combining prior spectral endmembers. The iteration schemes reduce the simulation error and improve the accuracy of the results. In comparative trials, the spectral angle, RMSE, and correlation coefficient of our method are 5.986, 284.6, and 0.905, respectively. Thus, our method outperforms other simulation methods.

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Yao, Lili, Qing Wang, Jinbo Yang, et al. "UAV-Borne Dual-Band Sensor Method for Monitoring Physiological Crop Status." Sensors 19, no. 4 (2019): 816. http://dx.doi.org/10.3390/s19040816.

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Unmanned aerial vehicles (UAVs) equipped with dual-band crop-growth sensors can achieve high-throughput acquisition of crop-growth information. However, the downwash airflow field of the UAV disturbs the crop canopy during sensor measurements. To resolve this issue, we used computational fluid dynamics (CFD), numerical simulation, and three-dimensional airflow field testers to study the UAV-borne multispectral-sensor method for monitoring crop growth. The results show that when the flying height of the UAV is 1 m from the crop canopy, the generated airflow field on the surface of the crop canopy is elliptical, with a long semiaxis length of about 0.45 m and a short semiaxis of about 0.4 m. The flow-field distribution results, combined with the sensor’s field of view, indicated that the support length of the UAV-borne multispectral sensor should be 0.6 m. Wheat test results showed that the ratio vegetation index (RVI) output of the UAV-borne spectral sensor had a linear fit coefficient of determination (R2) of 0.81, and a root mean square error (RMSE) of 0.38 compared with the ASD Fieldspec2 spectrometer. Our method improves the accuracy and stability of measurement results of the UAV-borne dual-band crop-growth sensor. Rice test results showed that the RVI value measured by the UAV-borne multispectral sensor had good linearity with leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW); R2 was 0.62, 0.76, and 0.60, and RMSE was 2.28, 1.03, and 10.73, respectively. Our monitoring method could be well-applied to UAV-borne dual-band crop growth sensors.

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Cuesta, J., M. Eremenko, X. Liu, et al. "Satellite observation of lowermost tropospheric ozone by multispectral synergism of IASI thermal infrared and GOME-2 ultraviolet measurements over Europe." Atmospheric Chemistry and Physics 13, no. 19 (2013): 9675–93. http://dx.doi.org/10.5194/acp-13-9675-2013.

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Abstract. We present a new multispectral approach for observing lowermost tropospheric ozone from space by synergism of atmospheric radiances in the thermal infrared (TIR) observed by IASI (Infrared Atmospheric Sounding Interferometer) and earth reflectances in the ultraviolet (UV) measured by GOME-2 (Global Ozone Monitoring Experiment-2). Both instruments are onboard the series of MetOp satellites (in orbit since 2006 and expected until 2022) and their scanning capabilities offer global coverage every day, with a relatively fine ground pixel resolution (12 km-diameter pixels spaced by 25 km for IASI at nadir). Our technique uses altitude-dependent Tikhonov–Phillips-type constraints, which optimize sensitivity to lower tropospheric ozone. It integrates the VLIDORT (Vector Linearized Discrete Ordinate Radiative Transfer) and KOPRA (Karlsruhe Optimized and Precise Radiative transfer Algorithm) radiative transfer codes for simulating UV reflectance and TIR radiance, respectively. We have used our method to analyse real observations over Europe during an ozone pollution episode in the summer of 2009. The results show that the multispectral synergism of IASI (TIR) and GOME-2 (UV) enables the observation of the spatial distribution of ozone plumes in the lowermost troposphere (LMT, from the surface up to 3 km a.s.l., above sea level), in good agreement with the CHIMERE regional chemistry-transport model. In this case study, when high ozone concentrations extend vertically above 3 km a.s.l., they are similarly observed over land by both the multispectral and IASI retrievals. On the other hand, ozone plumes located below 3 km a.s.l. are only clearly depicted by the multispectral retrieval (both over land and over ocean). This is achieved by a clear enhancement of sensitivity to ozone in the lowest atmospheric layers. The multispectral sensitivity in the LMT peaks at 2 to 2.5 km a.s.l. over land, while sensitivity for IASI or GOME-2 only peaks at 3 to 4 km a.s.l. at lowest (above the LMT). The degrees of freedom for the multispectral retrieval increase by 0.1 (40% in relative terms) with respect to IASI only retrievals for the LMT. Validations with ozonesondes (over Europe during summer 2009) show that our synergetic approach for combining IASI (TIR) and GOME-2 (UV) measurements retrieves lowermost tropospheric ozone with a mean bias of 1% and a precision of 16%, when smoothing by the retrieval vertical sensitivity (1% mean bias and 21% precision for direct comparisons).

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Bautista, Pinky A., and Yukako Yagi. "Digital simulation of staining in histopathology multispectral images: enhancement and linear transformation of spectral transmittance." Journal of Biomedical Optics 17, no. 5 (2012): 056013. http://dx.doi.org/10.1117/1.jbo.17.5.056013.

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Nigam, Rahul, Bimal K. Bhattacharya, Swapnil Vyas, and Markand P. Oza. "Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation." International Journal of Applied Earth Observation and Geoinformation 32 (October 2014): 173–85. http://dx.doi.org/10.1016/j.jag.2014.04.003.

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Claudio, Alimonti, Baiocchi Valerio, and Spadaro Carlo. "Integration of UAV data with soil water balance models for evaluation/monitoring of maize water stress." AUGUST 2020, no. 14(08):2020 (August 20, 2020): 1242–51. http://dx.doi.org/10.21475/ajcs.20.14.08.p2287.

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UAV based photogrammetry and 3D mapping are gaining fast and wide applications around the world majorly due to the relatively low-cost advantage it offers in the acquisition of high resolution multispectral acquisitions, compared to Aerophotogrammetry and satellite acquisitions. This research seeks to demonstrate the applicability of UAV photogrammetry visible, multispectral and thermal in investigating some physiological indexes of plants, reflecting plant physiological traits. A maize field in Latina (Italy) was acquired using a Fly Novex drone and with different cameras for the various acquisitions and consequently for different flight heights. The obtained images were processed using different photogrammetric models and a variable number of Ground Control Points (GCPs) for the georeferencing and accuracy assessment as well. Subsequently, by combining hydrological simulation methods and the use of physical indicators of the state of water stress, a method is proposed for predicting crop water consumption. The study conducted on the agricultural land of test site has provided useful results in terms of water savings, with an estimated value of three quarters of the total cubic meters of water needed to bring the land to saturation.

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Haas-Stapleton, Eric J., Miguel C. Barretto, Erika B. Castillo, Ryan J. Clausnitzer, and Robert L. Ferdan. "Assessing Mosquito Breeding Sites and Abundance Using An Unmanned Aircraft." Journal of the American Mosquito Control Association 35, no. 3 (2019): 228–32. http://dx.doi.org/10.2987/19-6835.1.

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ABSTRACT An unmanned aircraft system (UAS; i.e., drone) with an attached multispectral camera was used to quantify accumulated surface water on a 0.54-km2 tidal marsh that abuts San Francisco Bay, CA, USA. The results of the survey showed unequal accumulation of surface water and provided information for focused inspections of potential mosquito breeding areas and identified areas where existing ditches needed improvement for increasing water circulation in the marsh to reduce mosquito breeding. The UAS was also outfitted with a high-magnification zoom video camera and piloted at varying heights to measure the video camera's ability to visualize immature mosquitoes in 2 small containers of contrasting colors during simulation tests in a marsh habitat. Immature mosquitoes could be seen clearly in white or black containers at heights up to 14 and 8 m, respectively. An artificial intelligence algorithm identified mosquito larvae and pupae in videos of the white tray with 94.1% and 52.8% accuracy, respectively. Together, our studies show that an UAS equipped with multispectral and zoom cameras provides a means for vector control agencies to rapidly and quantitatively assess the landscape for the presence of surface water and mosquito larvae.

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Kang, Somi, Sean E. Lehman, Matthew V. Schulmerich, et al. "Refractive index sensing and surface-enhanced Raman spectroscopy using silver–gold layered bimetallic plasmonic crystals." Beilstein Journal of Nanotechnology 8 (November 24, 2017): 2492–503. http://dx.doi.org/10.3762/bjnano.8.249.

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Herein we describe the fabrication and characterization of Ag and Au bimetallic plasmonic crystals as a system that exhibits improved capabilities for quantitative, bulk refractive index (RI) sensing and surface-enhanced Raman spectroscopy (SERS) as compared to monometallic plasmonic crystals of similar form. The sensing optics, which are bimetallic plasmonic crystals consisting of sequential nanoscale layers of Ag coated by Au, are chemically stable and useful for quantitative, multispectral, refractive index and spectroscopic chemical sensing. Compared to previously reported homometallic devices, the results presented herein illustrate improvements in performance that stem from the distinctive plasmonic features and strong localized electric fields produced by the Ag and Au layers, which are optimized in terms of metal thickness and geometric features. Finite-difference time-domain (FDTD) simulations theoretically verify the nature of the multimode plasmonic resonances generated by the devices and allow for a better understanding of the enhancements in multispectral refractive index and SERS-based sensing. Taken together, these results demonstrate a robust and potentially useful new platform for chemical/spectroscopic sensing.

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