24.8.5 ATR -- IR, Infra-Red, Thermal, Applications

Pau, L.F., El Nahas, M.M.Y.,
An infra-red source classification system,
PR(14), No. 1-6, 1981, pp. 283-288.
Elsevier DOI 0309
BibRef

Chen, C.H., Yen, C.S.,
Object Isolation in FLIR Images using Fisher's Linear Discriminant,
PR(15), No. 3, 1982, pp. 153-159.
Elsevier DOI BibRef 8200

Sevigny, L., Hvedstrup-Jensen, G., Bohner, M., Ostevold, E., Grinaker, S., Dehne, J.,
Discrimination and Classification of Vehicles in Natural Scenes from Thermal Imagery,
CVGIP(24), No. 2, November 1983, pp. 229-243.
Elsevier DOI BibRef 8311

Gerhart, G.R., Bednarz, E.L., Meitzler, T.J., Sohn, E., Karlsen, R.E.,
Target Acquisition Methodology for Visual and Infrared Imaging Sensors,
OptEng(35), No. 10, October 1996, pp. 3026-3036. 9611
BibRef

Meitzler, T.J., Singh, H., Arefeh, L., Sohn, E., Gerhart, G.R.,
Predicting the Probability of Target Detection in Static Infrared and Visual Scenes Using the Fuzzy-Logic Approach,
OptEng(37), No. 1, January 1998, pp. 10-17. 9802
BibRef

Meitzler, T.J., Karlsen, R.E., Gerhart, G.R., Sohn, E., Singh, H.,
Wavelet Transforms of Cluttered Images and Their Application to Computing the Probability of Detection,
OptEng(35), No. 10, October 1996, pp. 3019-3025. 9611
Wavelets. BibRef

Meitzler, T.J., Singh, H.,
2-D Wavelet Image Transforms Extended to 3-D With Applications,
AeroSys(34), No. 3, July 1998, pp. 963-967. 9808
BibRef

Meitzler, T.J., Gerhart, G.R., Sohn, E., Singh, H.,
Detection Probability Using Relative Clutter in Infrared Images,
AeroSys(34), No. 3, July 1998, pp. 955-962. 9808
BibRef

Whiteley, M.R., Roggemann, M.C., Johnson, R.O., Rogers, S.K.,
Detection of Military Vehicles Using Infrared Spectral Radiometric Signatures,
OptEng(35), No. 12, December 1996, pp. 3531-3548. 9701
BibRef

Bernard, M.E.,
Detection of Airborne Compact Sources in Infra-Red Scenes Using Syntactic Pattern Recognition,
PRL(10), 1989, pp. 123-126. BibRef 8900

Der, S.Z., Chellappa, R.,
Probe-Based Automatic Target Recognition In Infrared Imagery,
IP(6), No. 1, January 1997, pp. 92-102.
IEEE DOI 9703
BibRef
Earlier:
Probe Based Recognition of Targets in Infrared Images,
CVPR94(870-875).
IEEE DOI Probes appear to be specific features computed from pairs of image points (a bunch of differences). BibRef

Lanterman, A.D., Miller, M.I., Snyder, D.L.,
General Metropolis-Hastings Jump Diffusions for Automatic Target Recognition in Infrared Scenes,
OptEng(36), No. 4, April 1997, pp. 1123-1137. 9705
BibRef

Ernisse, B., Rogers, S.K., Desimio, M.P., Raines, R.A.,
Complete Automatic Target Cuer/Recognition System for Tactical Forward-Looking Infrared Images,
OptEng(36), No. 9, September 1997, pp. 2593-2603. 9710
BibRef

Fairhurst, A.M., Lettington, A.H.,
Method of Predicting the Probability of Human Observers Recognizing Targets in Simulated Thermal Images,
OptEng(37), No. 3, March 1998, pp. 744-751. 9804
BibRef

Nandhakumar, N., Michel, J.D., Arnold, D.G., Tsihrintzis, G.A., Velten, V.,
Robust Thermophysics-Based Interpretation of Radiometrically Uncalibrated IR Images for ATR and Site Change Detection,
IP(6), No. 1, January 1997, pp. 65-78.
IEEE DOI 9703
BibRef
Earlier: A3, A2, A1, A4, A5: ARPA96(943-960).
See also Thermophysical Algebraic Invariants from Infrared Imagery for Object Recognition.
See also Invariants of the LWIR Thermophysical Model. BibRef

Michel, J.D., Nandhakumar, N., Saxena, T.[Tushar], Kapur, D.[Deepak],
Geometric, Algebraic, and Thermophysical Techniques for Object Recognition in IR Imagery,
CVIU(72), No. 1, October 1998, pp. 84-97.
DOI Link BibRef 9810

Nair, D., Aggarwal, J.K.,
Bayesian recognition of targets by parts in second generation forward looking infrared images,
IVC(18), No. 10, July 2000, pp. 849-864.
Elsevier DOI 0005
BibRef

Zhao, H., Shah, S., Choi, J.H., Nair, D., Aggarwal, J.K.,
Robust Automatic Target Detection/recognition System in Second Generation FLIR Imagery,
WACV98(262-263).
IEEE DOI 9809
BibRef

Aggarwal, J.K., Shah, S.[Shishir],
Object recognition and performance bounds,
CIAP97(I: 343-360).
Springer DOI 9709
BibRef

Nair, D., and Aggarwal, J.K.,
Robust Automatic Target Recognition in Second Generation FLIR Images,
WACV96(194-201).
IEEE DOI 9609
BibRef

Nair, D., Aggarwal, J.K.,
A Focused Target Segmentation Paradigm,
ECCV96(I:579-588).
Springer DOI ATR in FLIR BibRef 9600

Nair, D., Aggarwal, J.K.,
Region/Edge-Based Target Segmentation of FLIR Images Modeled by Weibull/Gaussian Distributions,
TRComputer and Vision Research Center 97-05-110. 9702
BibRef

de Ridder, D., Schutte, K., Schwering, P.,
Vehicle Recognition in Infrared Images Using Shared Weight Neural Networks,
OptEng(37), No. 3, March 1998, pp. 847-857. 9804
BibRef

Shah, S.[Shishir], Aggarwal, J.K.,
Multiple Feature Integration for Robust Object Localization,
CVPR98(765-771).
IEEE DOI BibRef 9800

Chen, H.X., Shen, Z.K., Shen, J.J.,
Method for Searching Bridge in IR Images,
AeroSysMag(13), No. 7, July 1998, pp. 21-24. 9808
BibRef

Liu, G.[Gang], Haralick, R.M.[Robert M.],
FLIR ATR Using Location Uncertainty,
JEI(9), No. 2, April 2000, pp. xx. BibRef 0004

Strehl, A.[Alexander], Aggarwal, J.K.,
MODEEP: A motion-based object detection and pose estimation method for airborne FLIR sequences,
MVA(11), No. 6, 2000, pp. 267-276.
Springer DOI 0005
BibRef

Strehl, A.[Alexander], Aggarwal, J.K.,
Detecting Moving Objects in Airborne Forward Looking Infra-Red Sequences,
CVBVS99(3).
IEEE DOI 9906
BibRef

Diani, M., Corsini, G., Baldacci, A.,
Space-time processing for the detection of airborne targets in IR image sequences,
VISP(148), No. 3, June 2001, pp. 151-157. 0108
BibRef

Li, B.X.[Bao-Xin], Zheng, Q.F.[Qin-Fen], Der, S.Z.[Sandor Z.], Chellappa, R.[Rama], Nasrabadi, N.M.[Nasser M.], Chan, L.A.[Lipchen A.], and Wang, L.C.[Lin-Cheng],
Experimental Evaluation of FLIR ATR Approaches-A Comparative Study,
CVIU(84), No. 1, October 2001, pp. 5-24.
DOI Link 0203
BibRef
Earlier: A1, A4, A2, A3, A5, A6, A7:
Empirical Evaluation of Neural, Statistical and Model-based Approaches to FLIR ATR,
UMD--TR3881, March 1998. Automatic Target Recognition. Performance Analysis. Evaluation of algorithms based on: convolution neural networks (CNN), principal component analysis (PCA), linear discriminant analysis (LDA), learning vector quantization (LVQ), and modular neural networks (MNN). And model based algorithms using: Hausdorff metric based matching and geometric hashing.
WWW Link. BibRef

Bharadwaj, P.[Priya], Carin, L.[Lawrence],
Infrared-Image Classification Using Hidden Markov Trees,
PAMI(24), No. 10, October 2002, pp. 1394-1398.
IEEE Abstract. 0210
Using tree description for segmented data. BibRef

Kim, S.H.[Sung-Ho], Kweon, I.S.[In-So],
3D target recognition using cooperative feature map binding under Markov Chain Monte Carlo,
PRL(27), No. 7, May 2006, pp. 811-821.
Elsevier DOI 3D infrared target recognition; Robust to noise; Cooperative feature map binding; Meaningful shape matching; MCMC optimization 0604
BibRef

Barshan, B.[Billur], Aytac, T.[Tayfun], Yuzbasioglu, C.[Cagri],
Target differentiation with simple infrared sensors using statistical pattern recognition techniques,
PR(40), No. 10, October 2007, pp. 2607-2620.
Elsevier DOI 0707
Target differentiation; Geometry differentiation; Surface differentiation; Statistical pattern recognition; Feature extraction; Infrared sensors; Optical sensing BibRef

Naeger, A.R., Christopher, S.A., Ferrare, R., Liu, Z.,
A New Technique Using Infrared Satellite Measurements to Improve the Accuracy of the CALIPSO Cloud-Aerosol Discrimination Method,
GeoRS(51), No. 1, January 2013, pp. 642-653.
IEEE DOI 1301
BibRef

Huo, H.Y.[Hong-Yuan], Jiang, X.G.[Xiao-Guang], Song, X.F.[Xian-Feng], Li, Z.L.[Zhao-Liang], Ni, Z.[Zhuoya], Gao, C.X.[Cai-Xia],
Detection of Coal Fire Dynamics and Propagation Direction from Multi-Temporal Nighttime Landsat SWIR and TIR Data: A Case Study on the Rujigou Coalfield, Northwest (NW) China,
RS(6), No. 2, 2014, pp. 1234-1259.
DOI Link 1403
BibRef

Sobrino, J.A., del Frate, F., Drusch, M., Jiménez-Muñoz, J.C., Manunta, P., Regan, A.,
Review of Thermal Infrared Applications and Requirements for Future High-Resolution Sensors,
GeoRS(54), No. 5, May 2016, pp. 2963-2972.
IEEE DOI 1604
geophysical techniques BibRef

Berg, A.[Amanda], Ahlberg, J.[Jörgen], Felsberg, M.[Michael],
Enhanced analysis of thermographic images for monitoring of district heat pipe networks,
PRL(83, Part 2), No. 1, 2016, pp. 215-223.
Elsevier DOI 1609
Remote thermography BibRef

Kim, S.[Sungho], Song, W.J.[Woo-Jin], Kim, S.H.[So-Hyun],
Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef
Earlier:
Infrared Variation Optimized Deep Convolutional Neural Network for Robust Automatic Ground Target Recognition,
PBVS17(195-202)
IEEE DOI 1709
Convolution, Feature extraction, Meteorology, Optimization, Target recognition, Thermal noise, Training BibRef

Liu, R.[Rang], Wang, D.[Dejiang], Jia, P.[Ping], Sun, H.[He],
An Omnidirectional Morphological Method for Aerial Point Target Detection Based on Infrared Dual-Band Model,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
Single point targets. BibRef

Caseiro, A.[Alexandre], Rücker, G.[Gernot], Tiemann, J.[Joachim], Leimbach, D.[David], Lorenz, E.[Eckehard], Frauenberger, O.[Olaf], Kaiser, J.W.[Johannes W.],
Persistent Hot Spot Detection and Characterisation Using SLSTR,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
Gas flares. BibRef

Guo, X.[Xing], Wu, Z.S.[Zhen-Sen], Wu, J.J.[Jia-Ji], Cao, Y.H.[Yun-Hua],
Study of infrared reflection characteristics of aerial target using MODIS data on GPU,
RealTimeIP(14), No. 3, October 2018, pp. 643-655.
WWW Link. 1811
BibRef

Yang, D.[Dong_Won],
Detail-enhanced target segmentation method for thermal video sequences based on spatiotemporal parameter update technique,
IET-IPR(13), No. 1, January 2019, pp. 216-223.
DOI Link 1812
BibRef

Xia, C.Q.[Chao-Qun], Li, X.R.[Xiao-Run], Zhao, L.Y.[Liao-Ying],
Infrared Small Target Detection via Modified Random Walks,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Silvestri, M.[Malvina], Rabuffi, F.[Federico], Pisciotta, A.[Antonino], Musacchio, M.[Massimo], Diliberto, I.S.[Iole Serena], Spinetti, C.[Claudia], Lombardo, V.[Valerio], Colini, L.[Laura], Buongiorno, M.F.[Maria Fabrizia],
Analysis of Thermal Anomalies in Volcanic Areas Using Multiscale and Multitemporal Monitoring: Vulcano Island Test Case,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Marotta, E.[Enrica], Peluso, R.[Rosario], Avino, R.[Rosario], Belviso, P.[Pasquale], Caliro, S.[Stefano], Carandente, A.[Antonio], Chiodini, G.[Giovanni], Macedonio, G.[Giovanni], Avvisati, G.[Gala], Marfè, B.[Barbara],
Thermal Energy Release Measurement with Thermal Camera: The Case of La Solfatara Volcano (Italy),
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Zhong, Y.F.[Yan-Fei], Xu, Y.[Yao], Wang, X.Y.[Xin-Yu], Jia, T.Y.[Tian-Yi], Xia, G.S.[Gui-Song], Ma, A.L.[Ai-Long], Zhang, L.P.[Liang-Pei],
Pipeline leakage detection for district heating systems using multisource data in mid- and high-latitude regions,
PandRS(151), 2019, pp. 207-222.
Elsevier DOI 1904
Pipeline leakage detection, Multisource data, Infrared imaging, Unmanned aerial vehicles, Mid- and high-latitude regions BibRef

Kim, S.[Sungho], Kim, J.[Jungho], Lee, J.[Jinyong], Ahn, J.[Junmo],
AS-CRI: A New Metric of FTIR-Based Apparent Spectral-Contrast Radiant Intensity for Remote Thermal Signature Analysis,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Chen, X.H.[Xue-Hong], Guo, Z.F.[Zheng-Fei], Chen, J.[Jin], Yang, W.[Wei], Yao, Y.M.[Yan-Ming], Zhang, C.[Chishan], Cui, X.H.[Xi-Hong], Cao, X.[Xin],
Replacing the Red Band with the Red-SWIR Band (0.74 rho red+0.26 rho swir) Can Reduce the Sensitivity of Vegetation Indices to Soil Background,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Guha, A.[Arindam], Yamaguchi, Y.S.[Yasu-Shi], Chatterjee, S.[Snehamoy], Rani, K.[Komal], Kumar, K.V.[Kumranchat Vinod],
Emittance Spectroscopy and Broadband Thermal Remote Sensing Applied to Phosphorite and Its Utility in Geoexploration: A Study in the Parts of Rajasthan, India,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Ortiz-Sanz, J.[Juan], Gil-Docampo, M.[Mariluz], Arza-García, M.[Marcos], Cañas-Guerrero, I.[Ignacio],
IR Thermography from UAVs to Monitor Thermal Anomalies in the Envelopes of Traditional Wine Cellars: Field Test,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

García-Sobrino, J.[Joaquín], Laparra, V.[Valero], Serra-Sagristà, J.[Joan], Calbet, X.[Xavier], Camps-Valls, G.[Gustau],
Improved Statistically Based Retrievals via Spatial-Spectral Data Compression for IASI Data,
GeoRS(57), No. 8, August 2019, pp. 5651-5668.
IEEE DOI 1908
IASI: Infrared Atmospheric Sounding Interferometer. data compression, geophysical techniques, IASI data, spatial compression, spectral compression, coding process, statistically based retrieval BibRef

Mullerova, D.[Daniela], Williams, M.[Meredith],
Satellite Monitoring of Thermal Performance in Smart Urban Designs,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Marchese, F.[Francesco], Genzano, N.[Nicola], Neri, M.[Marco], Falconieri, A.[Alfredo], Mazzeo, G.[Giuseppe], Pergola, N.[Nicola],
A Multi-Channel Algorithm for Mapping Volcanic Thermal Anomalies by Means of Sentinel-2 MSI and Landsat-8 OLI Data,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Faruolo, M.[Mariapia], Lacava, T.[Teodosio], Pergola, N.[Nicola], Tramutoli, V.[Valerio],
The VIIRS-Based RST-FLARE Configuration: The Val d'Agri Oil Center Gas Flaring Investigation in Between 2015-2019,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Li, X.L.[Xue-Lu], Monga, V.[Vishal], Mahalanobis, A.[Abhijit],
Multiview Automatic Target Recognition for Infrared Imagery Using Collaborative Sparse Priors,
GeoRS(58), No. 10, October 2020, pp. 6776-6790.
IEEE DOI 2009
Sparse matrices, Bayes methods, Optimization, Feature extraction, Slabs, Training, Dictionaries, spike and slab distribution BibRef

Meng, Z.G.[Zhi-Guo], Lei, J.[Jietao], Qian, Y.Q.[Yu-Qi], Xiao, L.[Long], Head, J.W.[James W.], Chen, S.[Shengbo], Cheng, W.M.[Wei-Ming], Shi, J.C.[Jian-Cheng], Ping, J.S.[Jin-Song], Kang, Z.Z.[Zhi-Zhong],
Thermophysical Features of the Rümker Region in Northern Oceanus Procellarum: Insights from CE-2 CELMS Data,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Ma, Y.[Yue], Wong, L.[Leslie], Vien, B.S.[Benjamin Steven], Kuen, T.[Thomas], Rajic, N.[Nik], Kodikara, J.[Jayantha], Chiu, W.K.[Wing Kong],
Quasi-Active Thermal Imaging of Large Floating Covers Using Ambient Solar Energy,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Lu, R.[Rong], Miskimins, J.L.[Jennifer L.], Zhizhin, M.[Mikhail],
Learning from Nighttime Observations of Gas Flaring in North Dakota for Better Decision and Policy Making,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Wang, Q.[Qi], Wu, W.Z.[Wen-Zhou], Su, F.Z.[Fen-Zhen], Xiao, H.[Han], Wu, Y.T.[Yu-Tong], Yao, G.B.[Guo-Biao],
Offshore Hydrocarbon Exploitation Observations from VIIRS NTL Images: Analyzing the Intensity Changes and Development Trends in the South China Sea from 2012 to 2019,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Yin, W.X.[Wen-Xin], Diao, W.H.[Wen-Hui], Wang, P.J.[Pei-Jin], Gao, X.[Xin], Li, Y.[Ya], Sun, X.[Xian],
PCAN: Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Liu, Y.X.[Yong-Xue], Zhi, W.F.[Wei-Feng], Xu, B.H.[Bi-Hua], Xu, W.X.[Wen-Xuan], Wu, W.[Wei],
Detecting high-temperature anomalies from Sentinel-2 MSI images,
PandRS(177), 2021, pp. 174-193.
Elsevier DOI 2106
High-temperature anomaly (HTA), Thermal anomaly index (TAI), Sentinel-2 Multispectral Instrument (MSI), Remote sensing BibRef

Gao, Y.Y.[Yan-Yan], Hao, M.[Ming], Wang, Y.J.[Yun-Jia], Dang, L.[Libo], Guo, Y.C.[Yue-Cheng],
Multi-Scale Coal Fire Detection Based on an Improved Active Contour Model from Landsat-8 Satellite and UAV Images,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Liu, J.L.[Jing-Long], Wang, Y.J.[Yun-Jia], Yan, S.Y.[Shi-Yong], Zhao, F.[Feng], Li, Y.[Yi], Dang, L.[Libo], Liu, X.X.[Xi-Xi], Shao, Y.Q.[Ya-Qin], Peng, B.[Bin],
Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Li, M.L.[Ming-Lei], Zhao, X.K.[Xing-Ke], Li, J.S.[Jia-Song], Nan, L.L.[Liang-Liang],
ComNet: Combinational Neural Network for Object Detection in UAV-Borne Thermal Images,
GeoRS(59), No. 8, August 2021, pp. 6662-6673.
IEEE DOI 2108
Object detection, Computational modeling, Feature extraction, Task analysis, Lighting, Neural networks, Image segmentation, thermal image BibRef

Zhizhin, M.[Mikhail], Matveev, A.[Alexey], Ghosh, T.[Tilottama], Hsu, F.C.[Feng-Chi], Howells, M.[Martyn], Elvidge, C.[Christopher],
Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Davis, D.S.[Dylan S.], Lundin, J.[Julius],
Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Ciazela, M.[Marta], Ciazela, J.[Jakub], Pieterek, B.[Bartosz],
High Resolution Apparent Thermal Inertia Mapping on Mars,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhao, C.H.[Chun-Hui], Wang, J.P.[Jin-Peng], Su, N.[Nan], Yan, Y.M.[Yi-Ming], Xing, X.W.[Xiang-Wei],
Low Contrast Infrared Target Detection Method Based on Residual Thermal Backbone Network and Weighting Loss Function,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Zhang, Z.H.[Zhi-Hua], Wang, D.[Difeng], Cheng, Y.[Yinhe], Gong, F.[Fang],
Long-Term Changes and Factors That Influence Changes in Thermal Discharge from Nuclear Power Plants in Daya Bay, China,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Vs, V.[Vibashan], Poster, D.[Domenick], You, S.[Suya], Hu, S.[Shuowen], Patel, V.M.[Vishal M.],
Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning,
WACV22(3697-3706)
IEEE DOI 2202
Lighting, Object detection, Detectors, Cameras, Semi- and Un- supervised Learning BibRef

Ma, C.H.[Cai-Hong], Yang, J.[Jin], Xia, W.[Wei], Liu, J.B.[Jian-Bo], Zhang, Y.F.[Yi-Fan], Sui, X.[Xin],
A Model for Expressing Industrial Information Based on Object-Oriented Industrial Heat Sources Detected Using Multi-Source Thermal Anomaly Data in China,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Zhang, R.H.[Rui-Heng], Xu, L.X.[Li-Xin], Yu, Z.Y.[Zheng-Yu], Shi, Y.[Ye], Mu, C.P.[Cheng-Po], Xu, M.[Min],
Deep-IRTarget: An Automatic Target Detector in Infrared Imagery Using Dual-Domain Feature Extraction and Allocation,
MultMed(24), No. 2022, pp. 1735-1749.
IEEE DOI 2204
Feature extraction, Object detection, Frequency-domain analysis, Resource management, Fourier transforms, object detection BibRef

Benhalouche, F.Z.[Fatima Zohra], Benharrats, F.[Farah], Bouhlala, M.A.[Mohammed Amine], Karoui, M.S.[Moussa Sofiane],
Spectral Unmixing Based Approach for Measuring Gas Flaring from VIIRS NTL Remote Sensing Data: Case of the Flare FIT-M8-101A-1U, Algeria,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Yu, B.[Bing], She, J.[Jie], Liu, G.X.[Guo-Xiang], Ma, D.Y.[De-Ying], Zhang, R.[Rui], Zhou, Z.W.[Zhi-Wei], Zhang, B.[Bo],
Coal fire identification and state assessment by integrating multitemporal thermal infrared and InSAR remote sensing data: A case study of Midong District, Urumqi, China,
PandRS(190), 2022, pp. 144-164.
Elsevier DOI 2208
Coal fire area identification, Thermal infrared remote sensing, Multitemporal InSAR, Coal fire area status quantitative evaluation BibRef

Sun, J.Q.[Jia-Qi], Wang, J.R.[Jia-Rong], Hao, Z.C.[Zhi-Cheng], Zhu, M.[Ming], Sun, H.J.[Hai-Jiang], Wei, M.[Ming], Dong, K.[Kun],
AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN+LSTM,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Faruolo, M.[Mariapia], Genzano, N.[Nicola], Marchese, F.[Francesco], Pergola, N.[Nicola],
A Tailored Approach for the Global Gas Flaring Investigation by Means of Daytime Satellite Imagery,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Chen, Y.[Ying], Wang, Z.[Zihan], Bai, X.Z.[Xiang-Zhi],
Fuzzy Sparse Subspace Clustering for Infrared Image Segmentation,
IP(32), 2023, pp. 2132-2146.
IEEE DOI 2304
Image segmentation, Clustering methods, Correlation, Sparse matrices, Optimization, Nonhomogeneous media, neighbor information BibRef

Yuan, S.[Shuai], Zheng, J.P.[Jue-Peng], Zhang, L.X.[Li-Xian], Dong, R.M.[Run-Min], Cheung, R.C.C.[Ray C. C.], Fu, H.H.[Hao-Huan],
MUREN: MUltistage Recursive Enhanced Network for Coal-Fired Power Plant Detection,
RS(15), No. 8, 2023, pp. 2200.
DOI Link 2305
BibRef

Zhu, Y.[Yun], Gong, C.J.[Cheng-Jian], Liu, S.W.[Shu-Wen], Yu, Z.Y.[Zhi-Yue], Shao, H.Z.[Han-Zeng], Yu, G.H.[Gao-Hang],
Infrared object detection via patch-tensor model and image denoising based on weighted truncated Schatten-p norm minimization,
IET-IPR(17), No. 6, 2023, pp. 1762-1774.
DOI Link 2305
alternating direction multiplier, infrared object detection, low-rank, non-local denoising, Schatten-p norm BibRef

Ding, C.[Chang], Luo, Z.D.[Zhen-Dong], Hou, Y.F.[Yi-Feng], Chen, S.Y.[Si-Yang], Zhang, W.D.[Wei-Dong],
An Effective Method of Infrared Maritime Target Enhancement and Detection with Multiple Maritime Scene,
RS(15), No. 14, 2023, pp. 3623.
DOI Link 2307
BibRef

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infrared imaging, Kalman filters, object tracking BibRef

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IEEE DOI 2411
Cognition, Target recognition, Semantics, Feature extraction, Atmospheric modeling, Aircraft, Predictive models, vision-inspired BibRef

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Training, Sensitivity, Target recognition, Noise, Training data, Detectors, Automatic Target Recognition, Infrared Images, Vision Transformer BibRef

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PBVS22(368-377)
IEEE DOI 2210
Training, Image coding, Sensitivity, Transform coding, Object detection, Transformers BibRef

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PBVS22(378-388)
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Training, Convolution, Computational modeling, Imaging, Object detection, Feature extraction, Computational efficiency BibRef

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Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
ATR -- Small Targets IR, Infra-Red, Thermal, Applications .