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Journal articles on the topic 'Imagerie LWIR'

Author: Grafiati

Published: 24 May 2025

Last updated: 31 July 2025

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1

Pelta, Ran, and Eyal Ben-Dor. "An Exploratory Study on the Effect of Petroleum Hydrocarbon on Soils Using Hyperspectral Longwave Infrared Imagery." Remote Sensing 11, no. 5 (2019): 569. http://dx.doi.org/10.3390/rs11050569.

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Manmade crude oil contamination, which has negative impacts on the environment and human health, can be found in various ecosystems all over the globe. Hyperspectral remote sensing (HRS) is an efficient tool to investigate this crude oil contamination where its electromagnetic spectrum is analyzed. This exploratory study used an innovative HRS imagery sensor to study the effect of petroleum hydrocarbon (PHC), found in crude oil, on the spectrum of soils across the longwave infrared (LWIR 8–12 μm) spectral region. This contrasts with previous studies that focused on shortwave and midwave infrar

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2

Yousefi, Bardia, Clemente Ibarra Castanedo, Émilie Bédard, Georges Beaudoin, and Xavier P. V. Maldague. "Mineral identification in LWIR hyperspectral imagery applying sparse-based clustering." Quantitative InfraRed Thermography Journal 16, no. 2 (2018): 147–62. http://dx.doi.org/10.1080/17686733.2018.1550902.

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3

Leblanc, George, Margaret Kalacska, J. Pablo Arroyo-Mora, Oliver Lucanus, and Andrew Todd. "A Practical Validation of Uncooled Thermal Imagers for Small RPAS." Drones 5, no. 4 (2021): 132. http://dx.doi.org/10.3390/drones5040132.

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Uncooled thermal imaging sensors in the LWIR (7.5 μm to 14 μm) have recently been developed for use with small RPAS. This study derives a new thermal imaging validation methodology via the use of a blackbody source (indoors) and real-world field conditions (outdoors). We have demonstrated this method with three popular LWIR cameras by DJI (Zenmuse XT-R, Zenmuse XT2 and, the M2EA) operated by three different popular DJI RPAS platforms (Matrice 600 Pro, M300 RTK and, the Mavic 2 Enterprise Advanced). Results from the blackbody work show that each camera has a highly linearized response (R2 >

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4

Fernández, Roemi, Héctor Montes, and Carlota Salinas. "VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity." Sensors 15, no. 6 (2015): 13994–4015. http://dx.doi.org/10.3390/s150613994.

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5

GOLDSTEIN, NEIL, PAJO VUJKOVIC-CVIJIN, MARSHA FOX, et al. "PROGRAMMABLE ADAPTIVE SPECTRAL IMAGERS FOR MISSION-SPECIFIC APPLICATION IN CHEMICAL/BIOLOGICAL SENSING." International Journal of High Speed Electronics and Systems 17, no. 04 (2007): 749–60. http://dx.doi.org/10.1142/s0129156407004953.

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An innovative passive standoff system for the detection of chemical/biological agents is described. The spectral, temporal and spatial resolution of the data collected are all adjustable in real time, making it possible to keep the tradeoff between the sensor operating parameters at optimum at all times. The instrument contains no macro-scale moving parts and is therefore an excellent candidate for the development of a robust, compact, lightweight and low-power-consumption sensor. The design can also serve as a basis for a wide variety of spectral instruments operating in the visible, NIR, MWI

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6

MESSINGER, DAVID W., CARL SALVAGGIO, and NATALIE M. SINISGALLI. "DETECTION OF GASEOUS EFFLUENTS FROM AIRBORNE LWIR HYPERSPECTRAL IMAGERY USING PHYSICS-BASED SIGNATURES." International Journal of High Speed Electronics and Systems 17, no. 04 (2007): 801–12. http://dx.doi.org/10.1142/s0129156407004990.

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Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and the concentration of the gas. All of these quantities vary spatially further complicating the detection problem. In complex scenes simple estimation of a “residual” spectrum may not be possible due to the variability in the scene background. A

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7

Yousefi, Bardia, Saeed Sojasi, Clemente Ibarra Castanedo, Xavier P. V. Maldague, Georges Beaudoin, and Martin Chamberland. "Continuum removal for ground-based LWIR hyperspectral infrared imagery applying non-negative matrix factorization." Applied Optics 57, no. 21 (2018): 6219. http://dx.doi.org/10.1364/ao.57.006219.

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8

Yousefi, Bardia, Clemente Ibarra Castanedo, Xavier P. V. Maldague, and Georges Beaudoin. "Assessing the reliability of an automated system for mineral identification using LWIR Hyperspectral Infrared imagery." Minerals Engineering 155 (August 2020): 106409. http://dx.doi.org/10.1016/j.mineng.2020.106409.

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9

Kniaz, V. V., V. V. Fedorenko, V. A. Mizginov, V. A. Knyaz, and W. Purgathofer. "FOREIGN OBJECT DETECTION IN TV AND LWIR IMAGERY OF A RUNWAY BY DEEP CONVOLUTIONAL NEURAL NETWORKS." Scientific Visualization 9, no. 4 (2017): 89–96. http://dx.doi.org/10.26583/sv.9.4.09.

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10

Nguyen, Tran Xuan Bach, Kent Rosser, and Javaan Chahl. "A Comparison of Dense and Sparse Optical Flow Techniques for Low-Resolution Aerial Thermal Imagery." Journal of Imaging 8, no. 4 (2022): 116. http://dx.doi.org/10.3390/jimaging8040116.

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It is necessary to establish the relative performance of established optical flow approaches in airborne scenarios with thermal cameras. This study investigated the performance of a dense optical flow algorithm on 14 bit radiometric images of the ground. While sparse techniques that rely on feature matching techniques perform very well with airborne thermal data in high-contrast thermal conditions, these techniques suffer in low-contrast scenes, where there are fewer detectable and distinct features in the image. On the other hand, some dense optical flow algorithms are highly amenable to para

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11

Felton, M., K. P. Gurton, J. L. Pezzaniti, D. B. Chenault, and L. E. Roth. "Measured comparison of the crossover periods for mid- and long-wave IR (MWIR and LWIR) polarimetric and conventional thermal imagery." Optics Express 18, no. 15 (2010): 15704. http://dx.doi.org/10.1364/oe.18.015704.

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12

Yousefi, Bardia, Clemente Ibarra-Castanedo, Martin Chamberland, Xavier P. V. Maldague, and Georges Beaudoin. "Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery." Remote Sensing 13, no. 11 (2021): 2125. http://dx.doi.org/10.3390/rs13112125.

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Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering

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13

Tappert, M. C., B. Rivard, A. Fulop та ін. "Characterizing Kimberlite Dilution by Crustal Rocks at the Snap Lake Diamond Mine (Northwest Territories, Canada) using SWIR (1.90–2.36μm) and LWIR (8.1–11.1μm) Hyperspectral Imagery Collected from Drill Core". Economic Geology 110, № 6 (2015): 1375–87. http://dx.doi.org/10.2113/econgeo.110.6.1375.

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14

Gallagher, James E., and Edward J. Oughton. "Assessing thermal imagery integration into object detection methods on air-based collection platforms." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-34791-8.

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AbstractObject detection models commonly focus on utilizing the visible spectrum via Red–Green–Blue (RGB) imagery. Due to various limitations with this approach in low visibility settings, there is growing interest in fusing RGB with thermal Long Wave Infrared (LWIR) (7.5–13.5 µm) images to increase object detection performance. However, we still lack baseline performance metrics evaluating RGB, LWIR and RGB-LWIR fused object detection machine learning models, especially from air-based platforms. This study undertakes such an evaluation, finding that a blended RGB-LWIR model generally exhibits

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15

Schmit, Joseph L. "Development of HgCdTe for LWIR Imagers." MRS Proceedings 90 (1986). http://dx.doi.org/10.1557/proc-90-27.

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ABSTRACTThis paper provides a historical perspective on the emergency of HgCdTe as the material of choice for long wavelength infrared (LWIR) imagers. The need for devices which see room temperature objects through the atmospheric window actually drove the development of this material. The lack of elemental or compound semiconductors having the desired wavelength response forced the choice of the alloy semiconductor, HgCdTe. The development of this material in several countries and companies beginning in the late 1950's is traced. The crystal growth methods used to grow HgCdTe have included me

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16

Bright, Benjamin C., Andrew T. Hudak, Nuria Sánchez-López, et al. "Comparing biomass consumption estimated from point cloud data versus long-wave infrared imagery during prescribed growing season burns in pine woodlands of the southeastern United States." International Journal of Wildland Fire 34, no. 7 (2025). https://doi.org/10.1071/wf24210.

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Background Researchers have developed technologies for fine-scale characterization of fuels via laser scanning and fine-scale measurement of surface fire behavior via terrestrial long-wave infrared (LWIR) imaging. Few studies have compared these technologies for their ability to estimate fuel consumption. Aims Here we compare fuel consumption estimated from point cloud data with fuel consumption estimated from LWIR imagery collected during prescribed burns of pine woodlands in the southeastern United States. Methods We adapted existing methods to estimate and map pre- and post-fire fuels and f

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17

Fernández, Roemi, Héctor Montes, and Carlota Salinas. "VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity." Sensors, June 15, 2015. https://doi.org/10.3390/s150613994.

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Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming. In order to alleviate these difficulties, this paper introduces an innovative sensory system based on Visible-Near InfraRed (VIS-NIR), Short-Wave InfraRed (SWIR) and Long-Wave InfraRed (LWIR) imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM cl

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18

Wang, Du, Lyuzhou Gao, Yanfei Zhong, and Liqin Cao. "A Statistical Split Window Method Without Atmospheric Profile Input for Temperature and Emissivity Retrieval from Airborne Long-Wavelenght Infrared Hyperspectral Imagery." Recent Advances in Remote Sensing, February 4, 2025, 1–4. https://doi.org/10.62880/rars25001.

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The retrieval of Land Surface Temperature (LST) and Emissivity (LSE) from long-wavelength thermal infrared (LWIR) hyperspectral data can be challenging due to uncertainties in atmospheric compensation (AC). While AC is typically performed using atmospheric radiance models, errors in the input atmospheric profiles can lead to significant inaccuracies. In this study, we propose an end-to-end method for retrieving LST and LSE without the need for local atmospheric profile inputs. The method consists of two main steps: first, a statistical split-window (SSW) method is used to estimate the ground l

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