Relevant bibliographies by topics / Imagerie LWIR

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Imagerie LWIR'

Author: Grafiati
Published: 24 May 2025
Last updated: 31 July 2025

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Journal articles on the topic "Imagerie LWIR"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Imagerie LWIR"

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Kubica, Romain. "Etude de diodes SOI verticales pour la détection LWIR." Electronic Thesis or Diss., Université Grenoble Alpes, 2025. http://www.theses.fr/2025GRALT001.

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Le sujet de la thèse concerne les imageurs infrarouge non refroidis. Plus particulièrementcelui-ci repose sur l’étude des microbolomètres, des détecteurs thermiques fonctionnantdans la bande LWIR (Long Wavelength InfraRed) entre 8 μm et 14 μm.Au cours des dernières années, le marché des microbolomètres s’est accru dans différentssecteurs industriels (automobile, spatial, surveillance) induisant ainsi des besoinspour une production à grande échelle et à faible coût. Contrairement aux filières classiquesde microbolomètres, qui utilisent des thermistances à base d’oxydes métalliques tels quedes o
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Higbee, Shawn. "A Bayesian approach to identification of gaseous effluents in passive LWIR imagery /." Online version of thesis, 2009. http://hdl.handle.net/1850/10969.

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Book chapters on the topic "Imagerie LWIR"

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Méndez, Heydi, Cesar San Martín, Josef Kittler, Yenisel Plasencia, and Edel García-Reyes. "Face Recognition with LWIR Imagery Using Local Binary Patterns." In Advances in Biometrics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01793-3_34.

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Dua, Mohit, Abhinav Mudgal, Mukesh Bhakar, Priyal Dhiman, and Bhagoti Choudhary. "K-Means and DNN-Based Novel Approach to Human Identification in Low Resolution Thermal Imagery." In Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-4444-0.ch002.

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In this chapter, a human detection system based on unsupervised learning method K-means clustering followed by deep learning approach You Only Look Once (YOLO) on thermal imagery has been proposed. Generally, images in the visible spectrum are used to conduct such human detection, which are not suitable for nighttime due to low visibility, hence for evaluation of our system. Hence, long wave infrared (LWIR) images have been used to implement the proposed work in this chapter. The system follows a two-step approach of generating anchor boxes using K-means clustering and then using those anchor
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MESSINGER, DAVID W., CARL SALVAGGIO, and NATALIE M. SINISGALLI. "DETECTION OF GASEOUS EFFLUENTS FROM AIRBORNE LWIR HYPERSPECTRAL IMAGERY USING PHYSICS-BASED SIGNATURES." In Spectral Sensing Research for Water Monitoring Applications and Frontier Science and Technology for Chemical, Biological and Radiological Defense. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812833242_0016.

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Conference papers on the topic "Imagerie LWIR"

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Zakizade, Elahe, Marvin Michel, Alexander Litke, Sebastian Blaeser, Sascha Weyers, and Dirk Weiler. "Technological concepts for broad- and narrow-band absorbers in uncooled thermal imagers visualizing MWIR and LWIR." In Electro-optical and Infrared Systems: Technology and Applications XXI, edited by Duncan L. Hickman, Helge Bürsing, Philip J. Soan, and Ove Steinvall. SPIE, 2024. http://dx.doi.org/10.1117/12.3031493.

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Holma, Hannu, Timo Hyvärinen, Jarmo Lehtomaa, Harri Karjalainen, and Risto Jaskari. "Advanced pushbroom hyperspectral LWIR imagers." In SPIE Defense, Security, and Sensing, edited by Mark A. Druy, Christopher D. Brown, and Richard A. Crocombe. SPIE, 2009. http://dx.doi.org/10.1117/12.818557.

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Shankar, M., R. Willett, N. Pitsianis, et al. "Ultra-Thin Multi-Aperture LWIR Imagers." In Frontiers in Optics. OSA, 2006. http://dx.doi.org/10.1364/fio.2006.fwn6.

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Holma, Hannu, Antti-Jussi Mattila, Timo Hyvärinen, and Oliver Weatherbee. "Advances in hyperspectral LWIR pushbroom imagers." In SPIE Defense, Security, and Sensing, edited by Mark A. Druy and Richard A. Crocombe. SPIE, 2011. http://dx.doi.org/10.1117/12.884078.

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Romano, Joao M., and Dalton S. Rosario. "Range invariant anomaly detection for LWIR polarimetric imagery." In 2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE, 2014. http://dx.doi.org/10.1109/aipr.2014.7041931.

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Rodger, Iain, Barry Connor, and Neil M. Robertson. "Classifying objects in LWIR imagery via CNNs." In SPIE Security + Defence, edited by David A. Huckridge, Reinhard Ebert, and Stephen T. Lee. SPIE, 2016. http://dx.doi.org/10.1117/12.2241858.

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Meigs, Andrew D., Leonard John Otten III, Tatyana Y. Cherezova, Bruce Rafert, and R. Glenn Sellar. "LWIR and MWIR ultraspectral Fourier transform imager." In Aerospace Remote Sensing '97, edited by Hiroyuki Fujisada. SPIE, 1997. http://dx.doi.org/10.1117/12.298109.

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Yang, Zhixiong, Chunchao Yu, Wei-jian Zheng, et al. "Radiation calibration for LWIR Hyperspectral Imager Spectrometer." In SPIE Asia Pacific Remote Sensing, edited by Allen M. Larar, Makoto Suzuki, and Jianyu Wang. SPIE, 2014. http://dx.doi.org/10.1117/12.2071097.

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Phillips, Mark C., and Bruce E. Bernacki. "Simultaneous standoff plume detection using a swept-ECQCL and passive hyperspectral imaging." In Optics and Photonics for Sensing the Environment. Optica Publishing Group, 2022. http://dx.doi.org/10.1364/es.2022.em4d.1.

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Direct comparisons are made between an active laser-based swept-wavelength ECQCL and passive LWIR hyperspectral imager for dynamic chemical plume detection at a standoff distance of 1.5 km in complex terrain.
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Müller, Thomas, and Daniel Manger. "Person detection in LWIR imagery using image retrieval." In SPIE Defense, Security, and Sensing, edited by Firooz A. Sadjadi and Abhijit Mahalanobis. SPIE, 2013. http://dx.doi.org/10.1117/12.2015853.

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Reports on the topic "Imagerie LWIR"

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Birch, Gabriel Carlisle, and Bryana Lynn Woo. Counter Unmanned Aerial Systems Testing: Evaluation of VIS SWIR MWIR and LWIR passive imagers. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1342469.

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Metoyer, Candace N., Stephen J. Walsh, Mark F. Tardiff, and Lawrence Chilton. Statistics for the Relative Detectability of Chemicals in Weak Gaseous Plumes in LWIR Hyperspectral Imagery. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/989458.

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