热红外高光谱遥感影像信息提取方法综述

曹丽琴; 汪都; 熊海洋; 钟燕飞

doi:10.11834/jig.230738

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热红外高光谱遥感影像信息提取方法综述
Review of information extraction methods from thermal infrared hyperspectral remote sensing images
2024年29卷第8期页码：2089-2112
纸质出版日期： 2024-08-16 ，
DOI： 10.11834/jig.230738
稿件说明：

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曹丽琴，汪都，熊海洋，钟燕飞. 2024. 热红外高光谱遥感影像信息提取方法综述. 中国图象图形学报， 29(08):2089-2112

Cao Liqin， Wang Du， Xiong Haiyang， Zhong Yanfei. 2024. Review of information extraction methods from thermal infrared hyperspectral remote sensing images. Journal of Image and Graphics， 29(08):2089-2112
曹丽琴，汪都，熊海洋，钟燕飞. 2024. 热红外高光谱遥感影像信息提取方法综述. 中国图象图形学报， 29(08):2089-2112 DOI： 10.11834/jig.230738.

Cao Liqin， Wang Du， Xiong Haiyang， Zhong Yanfei. 2024. Review of information extraction methods from thermal infrared hyperspectral remote sensing images. Journal of Image and Graphics， 29(08):2089-2112 DOI： 10.11834/jig.230738.

摘要

热红外高光谱遥感影像蕴含着丰富的光谱特性和温度信息，能够反映出地物、气体等特有的诊断特征，在矿物识别、环境监测和军事等众多领域发挥着重要作用。然而，由于热红外高光谱观测数据受到地表温度和发射率、大气环境和仪器测量噪声等共同影响，引起背景噪声与目标信号差异较小、耦合信号无法精确分离等问题，使得热红外高光谱影像信息提取存在巨大的挑战，难以有效实施应用。针对热红外高光谱信息提取的研究进展和现存难点，本文系统梳理了热红外高光谱影像信息提取方法，主要包括地表温度和发射率地表参量反演、热红外高光谱混合光谱分解、影像分类及目标探测。在此基础上，总结现有热红外高光谱信息提取的现状和问题，包括高光谱丰富光谱信息在地气参数反演方面尚未得到充分的利用，基于深度学习理论实现热红外高光谱地表参量反演及混合像元分解、地物分类、目标探测应用仍处于起步阶段且面临数据集匮乏。如何充分利用热红外高光谱密集通道，融合物理模型和深度学习理论实现智能化、高精度的地气参数一体化反演，并在此基础上进行热红外高光谱混合像元分解、地物分类、目标探测是热红外高光谱遥感影像信息提取未来可能的发展趋势和研究方向。

Abstract

Longwave infrared （LWIR） hyperspectral remote sensing images offer a wealth of spectral information alongside land surface temperature （LST） data， which make them invaluable for discerning solid-phase materials and gases. This capability holds significant implications across diverse domains， including mineral identification， environmental monitoring， and military applications. However， the underlying phenomenology and environmental interactions of emissive regions within the LWIR spectrum significantly diverge from those observed in reflective regions. This divergence impacts various facets of thermal infrared （IR） hyperspectral image （HSI） analysis， which span from sensor design considerations to data exploitation methodologies. Compounding this complexity are the intertwined influences of factors such as LST， emissivity， atmospheric profiles， and instrumental noise， which lead to challenges such as subtle distinctions between background noise and target signals， as well as inaccuracies in signal separation within thermal IR hyperspectral observation data. Consequently， the effective extraction of thermal IR HSI information poses formidable challenges for practical application implementation. In this study， we systematically review methods for LWIR HSI information extraction by drawing upon ongoing research progress and addressing prevailing challenges in LIWR hyperspectral remote sensing. Our examination encompasses four primary areas： 1） LST and emissivity inversion： LWIR hyperspectral remote sensing serves as a potent tool for large-scale LST and land surface emissivity （LSE） monitoring. However， the accurate retrieval of LST and LSE is fraught with complexity owing to their intricate coupling with atmospheric components， as delineated by the radiative transfer equation. We discuss two broad approaches， namely， the two-step method and the integration method， for mitigating this ill-posed problem. The former entails atmospheric compensation （AC） and temperature and emissivity separation （TES）， where AC filters out atmospheric influences to isolate ground-leaving radiance from at-sensor radiance. Subsequently， TES methods are employed to estimate LST and LSE. Given the propensity for inaccurate AC to introduce accumulation errors and compromise retrieval accuracy， integration methods capable of simultaneous AC and TES are also reviewed， with deep learning-driven methods exemplifying a typical integration approach. 2） LWIR hyperspectral mixed spectral decomposition： spectral mixture analysis （SMA） involves identifying and extracting endmember spectra in a scene to determine the abundance of each endmember within each pixel. Most applications of LWIR SMA focus on mineral detection and classification. Unlike the reflectance of a mixed pixel， which is defined as a linear combination within the pixel， the emissivity of a mixed pixel is not as straightforward to define because the measured radiance depends on the emissivity and temperature of each material. The mixed spectral decomposition methods for isothermal and non-isothermal pixels are discussed. When a pixel is isothermal， the isothermal mixture model is identical to the mixture for reflectance after removing the temperature component. However， as a pixel becomes non-isothermal， unmixing methods are necessary to handle the nonlinearity resulting from temperature variations. This study summarizes all the methods and highlights the challenges associated with SMA. 3） Classification： classification tasks in the LWIR domain involve successfully modeling and classifying background materials， such as minerals and vegetation mapping. However， the scarcity of prominent spectral features in the LWIR spectrum complicates the remote differentiation of natural land surfaces. For instance， various materials such as paints， water， soil， road surfaces， and vegetation exhibit spectral emissivities ranging between 0.8 and 0.95. Moreover， although spectral emissivity variations exist among different materials， they are less conspicuous compared with the reflective region. In addition， the uncertainty associated with retrieved emissivity challenges the classification of different materials. This study reviews traditional machine learning classification methods， including spectral-based， spatial-based， and spectral-spatial integration-based methods， alongside deep learning approaches， by summarizing the advancements in these processing methods. 4） Target detection： target detection encompasses solid- and gas-phase targets. The algorithms employed for LWIR HSI analysis， which is similar to those used for visible-near IR and shortwave IR HIS， have reached a mature stage and are reviewed in this work， including matched filter-type algorithms， among others. Challenges in target detection mirror those encountered in classification tasks， given that spectral emissivities in the LWIR tend to be smaller than the corresponding spectral reflectance variations observed in the reflective region. Consequently， the performance of solid-phase target detection algorithms in real-world applications is impacted， particularly by their sensitivity to target-background model mismatch arising from similar emissivities and other errors. By contrast， gas detection in the LWIR domain relies on selective absorption and emission phenomena， particularly by chemical vapors， which exhibit narrow spectral features. Although gas plumes may span a large number of pixels， their detection depends on factors such as concentration， signature strength， and temperature contrast with the background materials. Consequently， chemical detection applications necessitate rigorous physical processes in airborne （down-looking） and standoff （side-looking） configurations. In addition to employing strict physical and statistical models， gas detection methodologies are increasingly integrating deep learning models. This trend reflects the recognition of the potential of deep learning in enhancing the capabilities of gas detection algorithms. Our discourse concludes with a discussion on the future trajectory and research direction of extracting information from thermal IR hyperspectral remote sensing images. Despite the continual integration of new technical methodologies such as deep learning， the computational intricacies inherent in LWIR hyperspectral remote sensing underscore the necessity of approaches that combine physical mechanisms with machine learning models. These hybrid methodologies hold promise in addressing the multifaceted challenges associated with LWIR HSI analysis， which paves the way for enhanced information extraction and practical application implementations.

关键词

热红外高光谱地表参量反演混合光谱分解影像分类目标探测

Keywords

infrared hyperspectralland surface parameter retrievalspectral unmixingclassificationtarget detection

references

Acito N， Diani M and Corsini G. 2013. Hyperspectral signal subspace identification in the presence of rare vectors and signal-dependent noise. IEEE Transactions on Geoscience and Remote Sensing， 51（1）： 283-299 ［DOI： 10.1109/TGRS.2012.2201488http://dx.doi.org/10.1109/TGRS.2012.2201488］

Acito N， Diani M and Corsini G. 2019. Coupled subspace-based atmospheric compensation of LWIR hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing， 57（8）： 5224-5238 ［DOI： 10.1109/TGRS.2019.2897498http://dx.doi.org/10.1109/TGRS.2019.2897498］

Aslett Z， Taranik J V and Riley D N. 2018. Mapping rock forming minerals at Boundary Canyon， Death Valey National Park， California， using aerial SEBASS thermal infrared hyperspectral image data. International Journal of Applied Earth Observation and Geoinformation， 64： 326-339 ［DOI： 10.1016/j.jag.2017.08.001http://dx.doi.org/10.1016/j.jag.2017.08.001］

Baatz M and Schape A. 2000. Multiresolution segmentation： an optimization approach for high quality multi-scale image segmentation//Proceedings of the Angewandte Geographische Informations-Verarbeitung. Karlsruhe， Germany：［s.n.］： 12-23

Bandfield J L. 2002. Global mineral distributions on Mars. Journal of Geophysical Research： Planets， 107（E6）： #5042 ［DOI： 10.1029/2001JE001510http://dx.doi.org/10.1029/2001JE001510］

Bandos T V， Bruzzone L and Camps-Valls G. 2009. Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing， 47（3）： 862-873 ［DOI： 10.1109/TGRS.2008.2005729http://dx.doi.org/10.1109/TGRS.2008.2005729］

Bao F L， Wang X J， Sureshbabu S H， Sreekumar G， Yang L P， Aggarwal V， Boddeti V N and Jacob Z. 2023. Heat-assisted detection and ranging. Nature， 619（7971）： 743-748 ［DOI： 10.1038/s41586-023-06174-6http://dx.doi.org/10.1038/s41586-023-06174-6］

Barisione F， Solarna D， De Giorgi A， Moser G and Serpico S B. 2016. Supervised classification of thermal infrared hyperspectral images through bayesian， markovian， and region-based approaches//2016 IEEE International Geoscience and Remote Sensing Symposium （IGARSS）. Beijing， China： IEEE： 937-940 ［DOI： 10.1109/IGARSS.2016.7729237http://dx.doi.org/10.1109/IGARSS.2016.7729237］

Benediktsson J A， Palmason J A and Sveinsson J R. 2005. Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing， 43（3）： 480-491 ［DOI： 10.1109/TGRS.2004.842478http://dx.doi.org/10.1109/TGRS.2004.842478］

Berk A， Conforti P， Kennett R， Perkins T， Hawes F and van den Bosch J. 2014. MODTRAN6： a major upgrade of the MODTRAN radiative transfer code//Proceedings of the Algorithms and Technologies for Multispectral， Hyperspectral， and Ultraspectral Imagery XX. Baltimore， USA： SPIE： #90880H ［DOI： 10.1117/12.2050433http://dx.doi.org/10.1117/12.2050433］

Borel C. 2008. Error analysis for a temperature and emissivity retrieval algorithm for hyperspectral imaging data. International Journal of Remote Sensing， 29（17/18）： 5029-5045 ［DOI： 10.1080/01431160802036540http://dx.doi.org/10.1080/01431160802036540］

Borel C C. 1997. Iterative retrieval of surface emissivity and temperature for a hyperspectral sensor//Proceedings of the first JPL Workshop on Remote Sensing of Land Surface Emissivity.Pasadena， USA：［l.n.］： 1-5

Campos R L， Yoon S C， Chung S and Bhandarkar S M. 2023. Semisupervised deep learning for the detection of foreign materials on poultry meat with near-infrared hyperspectral imaging. Sensors， 23（16）： #7014 ［DOI： 10.3390/s23167014http://dx.doi.org/10.3390/s23167014］

Camps-Valls G， Gomez-Chova L， Munoz-Mari J， Vila-Frances J and Calpe-Maravilla J. 2006. Composite kernels for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters， 3（1）： 93-97 ［DOI： 10.1109/LGRS.2005.857031http://dx.doi.org/10.1109/LGRS.2005.857031］

Cao L Q， He J N， Gao L Z， Zhong Y F， Hu X and Li Z J. 2022. LWIR hyperspectral image classification based on a temperature-emissivity residual network and conditional random field model. International Journal of Remote Sensing， 43（10）： 3744-3768 ［DOI： 10.1080/01431161.2022.2105667http://dx.doi.org/10.1080/01431161.2022.2105667］

Carion N， Massa F， Synnaeve G， Usunier N， Kirillov A and Zagoruyko S. 2020. End-to-end object detection with Transformers//Proceedings of the 16th European Conference on Computer Vision. Glasgow， UK： Springer： 213-229 ［DOI： 10.1007/978-3-030-58452-8_13http://dx.doi.org/10.1007/978-3-030-58452-8_13］

Chang C I. 2000. An information-theoretic approach to spectral variability， similarity， and discrimination for hyperspectral image analysis. IEEE Transactions on Information Theory， 46（5）： 1927-1932 ［DOI： 10.1109/18.857802http://dx.doi.org/10.1109/18.857802］

Chen M S， Ni L， Jiang X G， Li Z L and Wu H. 2018. Retrieval of atmospheric and land surface parameters from satellite-based thermal infrared hyperspectral data using an artificial neural network technique//IGARSS， 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia， Spain： IEEE： 2745-2748 ［DOI： 10.1109/IGARSS.2018.8518131http://dx.doi.org/10.1109/IGARSS.2018.8518131］

Chen S S， Ren H Z， Liu R Y， Tao Y Z， Zheng Y T and Liu H C， 2021. Mapping sandy land using the new sand differential emissivity index from thermal infrared emissivity data. IEEE Transactions on Geoscience and Remote Sensing， 59（7）： 5464-5478 ［DOI： 10.1109/TGRS.2020.3022772http://dx.doi.org/10.1109/TGRS.2020.3022772］

Cheng J， Liang S L， Wang J D and Li X W. 2010. A stepwise refining algorithm of temperature and emissivity separation for hyperspectral thermal infrared data. IEEE Transactions on Geoscience and Remote Sensing， 48（3）： 1588-1597 ［DOI： 10.1109/TGRS.2009.2029852http://dx.doi.org/10.1109/TGRS.2009.2029852］

Christensen P R， Bandfield J L， Smith M D， Hamilton V E and Clark R N. 2000. Identification of a basaltic component on the martian surface from thermal emission spectrometer data. Journal of Geophysical Research： Planets， 105（E4）： 9609-9621 ［DOI： 10.1029/1999JE001127http://dx.doi.org/10.1029/1999JE001127］

Chutia D， Bhattacharyya D. K， Sarma K. K. Kalita R and Sudhakar S. 2016. hyperspectral remote sensing classifications： a perspective survey. Transactions in GIS， 20（4）： 463-490 ［DOI：10.1111/tgis.12164http://dx.doi.org/10.1111/tgis.12164］

Clare P. 2006. Design and modeling of spectral-thermal unmixing targets for airborne hyperspectral imagery//Proceedings of the Algorithms and Technologies for Multispectral， Hyperspectral， and Ultraspectral Imagery XII. Orlando， USA： SPIE： 542-553 ［DOI： 10.1117/12.665821http://dx.doi.org/10.1117/12.665821］

Cubero-Castan M， Chanussot J， Achard V， Briottet X and Shimoni M. 2015. A physics-based unmixing method to estimate subpixel temperatures on mixed pixels. IEEE Transactions on Geoscience and Remote Sensing， 53（4）： 1894-1906 ［DOI： 10.1109/TGRS.2014.2350771http://dx.doi.org/10.1109/TGRS.2014.2350771］

Cui C Y， Wang X Y， Wang S Y， Zhang L P and Zhong Y F. 2023. Unrolling nonnegative matrix factorization with group sparsity for blind hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing， 61： #5516712 ［DOI： 10.1109/TGRS.2023.3292453http://dx.doi.org/10.1109/TGRS.2023.3292453］

Cui F X， Li D C， Wu J， Wang A J and Li Y Y. 2019. Adaptive feature extraction algorithm based on Lasso method for detecting polluted gas. Acta Optica Sinica， 39（5）： 406-414

崔方晓，李大成，吴军，王安静，李扬裕. 2019. 基于Lasso方法的污染气体自适应探测算法. 光学学报， 39（5）： 406-414 ［DOI： 10.3788/AOS201939.0530003http://dx.doi.org/10.3788/AOS201939.0530003］

De Carvalho Jr O A and Meneses P R. 2000. Spectral correlation mapper （SCM）： an improvement on the spectral angle mapper （SAM）. Summaries of the 9th JPL Airborne Earth Science Workshop. Pasadena， USA： JPL Publication： #2

DiPietro R S， Manolakis D， Lockwood R B， Cooley T and Jacobson J. 2012. Hyperspectral matched filter with false-alarm mitigation. Optical Engineering， 51（1）： #016202 ［DOI： 10.1117/1.OE.51.1.016202http://dx.doi.org/10.1117/1.OE.51.1.016202］

Farley V， Chamberland M， Lagueux P， Vallières A， Villemaire A and Giroux J. 2007. Chemical agent detection and identification with a hyperspectral imaging infrared sensor//Proceedings of the Imaging Spectrometry XII. San Diego， USA： SPIE： 334-345 ［DOI： 10.1117/12.736731http://dx.doi.org/10.1117/12.736731］

Fauvel M， Benediktsson J A， Chanussot J and Sveinsson J R. 2008. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Transactions on Geoscience and Remote Sensing， 46（11）： 3804-3814 ［DOI： 10.1109/TGRS.2008.922034http://dx.doi.org/10.1109/TGRS.2008.922034］

Feng R Y， Wang L Z and Zeng T Y. 2023. Review of hyperspectral remote sensing image subpixel information extraction. Acta Geodaetica et Cartographica Sinica， 52（7）： 1187-1201

冯如意，王力哲，曾铁勇. 2023. 高光谱遥感图像亚像元信息提取方法综述. 测绘学报， 52（7）： 1187-1201 ［DOI： 10.11947/j.AGCS.2023.20220491http://dx.doi.org/10.11947/j.AGCS.2023.20220491］

Gålfalk M， Olofsson G， Crill P and Bastviken D. 2016. Making methane visible. Nature Climate Change， 6（4）： 426-430 ［DOI： 10.1038/NCLIMATE2877http://dx.doi.org/10.1038/NCLIMATE2877］

Gao L Z， Zhong Y F， Cao L Q， He J N and Zhu X H. 2022. A practical temperature and emissivity separation framework with reanalysis atmospheric profiles for hyper-cam airborne thermal infrared hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 15： 687-699 ［DOI： 10.1109/JSTARS.2021.3136194http://dx.doi.org/10.1109/JSTARS.2021.3136194］

Gastellu-Etchegorry J P， Yin T G， Lauret N， Cajgfinger T， Gregoire T， Grau E， Feret J B， Lopes M， Guilleux J， Dedieu G， Malenovský Z， Cook B D， Morton D， Rubio J， Durrieu S， Cazanave G， Martin E and Ristorcelli T. 2015. Discrete anisotropic radiative transfer （DART 5） for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes. Remote Sensing， 7（2）： 1667-1701 ［DOI： 10.3390/rs70201667http://dx.doi.org/10.3390/rs70201667］

Gillespie A， Rokugawa S， Matsunaga T， Cothern J S， Hook S and Kahle A B. 1998. A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer （ASTER） images. IEEE Transactions on Geoscience and Remote Sensing， 36（4）： 1113-1126 ［DOI： 10.1109/36.700995http://dx.doi.org/10.1109/36.700995］

Goudge T A， Mustard J F， Head J W， Salvatore M R and Wiseman S M. 2015. Integrating CRISM and TES hyperspectral data to characterize a halloysite-bearing deposit in Kashira crater， Mars. Icarus， 250： 165-187 ［DOI： 10.1016/j.icarus.2014.11.034http://dx.doi.org/10.1016/j.icarus.2014.11.034］

Granero-Belinchon C， Michel A， Achard V and Briottet X. 2020. Spectral unmixing for thermal infrared multi-spectral airborne imagery over urban environments： day and night synergy. Remote Sensing， 12（11）： #1871 ［DOI： 10.3390/rs12111871http://dx.doi.org/10.3390/rs12111871］

Gu D， Gillespie A R， Kahle A B and Palluconi F D. 2000. Autonomous atmospheric compensation （AAC） of high resolution hyperspectral thermal infrared remote-sensing imagery. IEEE Transactions on Geoscience and Remote Sensing， 38（6）： 2557-2570 ［DOI： 10.1109/36.885203http://dx.doi.org/10.1109/36.885203］

Hackwell J A， Warren D W， Bongiovi R P， Hansel S J， Hayhurst T L， Mabry D J， Sivjee M G and Skinner J W. 1996. LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing//Proceedings of the Imaging Spectrometry II. Denver， USA： SPIE： 102-107 ［DOI： 10.1117/12.258057http://dx.doi.org/10.1117/12.258057］

Hamilton V E. 2000. Thermal infrared emission spectroscopy of the pyroxene mineral series. Journal of Geophysical Research， 105： 9701–9716 ［DOI： 10.1029/1999JE001112http://dx.doi.org/10.1029/1999JE001112］

Hook S J， Johnson W R and Abrams M J. 2013. NASA’s hyperspectral thermal emission spectrometer （HyTES）//Kuenzer C and Dech S， eds. Thermal Infrared Remote Sensing： Sensors， Methods， Applications. Dordrecht， the Netherlands： Springer： 93-115 ［DOI： 10.1007/978-94-007-6639-6_5http://dx.doi.org/10.1007/978-94-007-6639-6_5］

Hu X， Zhong Y F， Wang X Y， Luo C， Zhao J， Lei L and Zhang L P. 2022. SPNet： spectral patching end-to-end classification network for UAV-borne hyperspectral imagery with high spatial and spectral resolutions. IEEE Transactions on Geoscience and Remote Sensing， 60： #5503417 ［DOI： 10.1109/TGRS.2021.3049292http://dx.doi.org/10.1109/TGRS.2021.3049292］

Hulley G C， Duren R M， Hopkins F M， Hook S J， Vance N， Guillevic P， Johnson W R， Eng B T， Mihaly J M， Jovanovic V M， Chazanoff S L， Staniszewski Z K， Kuai L， Worden J， Frankenberg C， Rivera G， Aubrey A D， Miller C E， Malakar N K， Tom􀅡s J M S and Holmes K T. 2016. High spatial resolution imaging of methane and other trace gases with the airborne hyperspectral thermal emission spectrometer （HyTES）. Atmospheric Measurement Techniques， 9（5）： 2393-2408 ［DOI： 10.5194/amt-9-2393-2016http://dx.doi.org/10.5194/amt-9-2393-2016］

Jia Z Y. 2022. Gas Plume Imaging Simulation and Detection Method Based on Fourier Transform Thermal Infrared Hyperspectral. Wuhan University

贾朝阳. 2022. 基于傅立叶变换热红外高光谱的气体成像模拟与探测方法研究. 武汉大学

Jimenez-Munoz J C and Sobrino J A. 2010. A single-channel algorithm for land-surface temperature retrieval from ASTER data. IEEE Geoscience and Remote Sensing Letters， 7（1）： 176-179 ［DOI： 10.1109/LGRS.2009.2029534http://dx.doi.org/10.1109/LGRS.2009.2029534］

Jr DiStasio R J and Resmini R G. 2010. Atmospheric compensation of thermal infrared hyperspectral imagery with the emissive empirical line method and the in-scene atmospheric compensation algorithms： a comparison//Proceedings of the Algorithms and Technologies for Multispectral， Hyperspectral， and Ultraspectral Imagery XVI. ［s.l.］： SPIE： 805-816 ［DOI： 10.1117/12.849898http://dx.doi.org/10.1117/12.849898］

Kim S H， Ma J R， Kook M J and Lee K S. 2005. Current status of hyperspectral remote sensing： principle， data processing techniques， and applications. Korean Journal of Remote Sensing， 21（4）： 341-369 ［DOI： 10.7780/kjrs.2005.21.4.341http://dx.doi.org/10.7780/kjrs.2005.21.4.341］

Kraut S and Scharf L L. 1999. The CFAR adaptive subspace detector is a scale-invariant GLRT. IEEE Transactions on Signal Processing， 47（9）： 2538-2541 ［DOI： 10.1109/78.782198http://dx.doi.org/10.1109/78.782198］

Kraut S， Scharf L L and McWhorter L T. 2001. Adaptive subspace detectors. IEEE Transactions on Signal Processing， 49（1）： 1-16 ［DOI： 10.1109/78.890324http://dx.doi.org/10.1109/78.890324］

Kuenzer C and Dech S. 2013. Thermal Infrared Remote Sensing： Sensors， Methods， Applications. Dordrecht， the Netherlands： Springer： 978-994

Kumar S， Torres C， Ulutan O， Ayasse A， Roberts D and Manjunath B S. 2020. Deep remote sensing methods for methane detection in overhead hyperspectral imagery//Proceedings of 2020 IEEE Winter Conference on Applications of Computer Vision （WACV）. Snowmass， USA： IEEE： 1765-1774 ［DOI： 10.1109/WACV45572.2020.9093600http://dx.doi.org/10.1109/WACV45572.2020.9093600］

Lagueux P， Farley V， Chamberland M， Villemaire A， Turcotte C， Puckrin E and TELOPS INC （QUEBEC）. 2009. Design and performance of the hyper-cam， an infrared hyperspectral imaging sensor ［EB/OL］. ［2023-10-02］. https://apps.dtic.mil/sti/citations/ADA568314https://apps.dtic.mil/sti/citations/ADA568314

Larrieux E R. 2009. Performance Evaluation of Chemical Plume Detection and Quantification Algorithms. Boston， USA： Northeastern University

Li C L， Liu C Y， Jin J， Xu R， Lyu G， Xie J N， Yuan L Y， Liu S J and Wang J Y. 2022. Development of infrared hyperspectral remote sensing imaging and application of gas detection （invited）. Infrared and Laser Engineering， 51（7）： #20210866

李春来，刘成玉，金健，徐睿，吕刚，谢嘉楠，袁立银，刘世界，王建宇. 2022. 红外高光谱遥感成像的技术发展与气体探测应用（特邀）. 红外与激光工程， 51（7）： #20210866 ［DOI： 10.3788/IRLA20210866http://dx.doi.org/10.3788/IRLA20210866］

Li S T， Song W W， Fang L Y， Chen Y S， Ghamisi P and Benediktsson J.A. 2019. Deep learning for hyperspectral image classification： An overview. IEEE Transactions on Geoscience and Remote Sensing， 57： 6690-6709 ［DOI： 10.1109/TGRS.2019.2907932http://dx.doi.org/10.1109/TGRS.2019.2907932］

Li X Y， Li Z M， Qiu H M， Hou G L and Fan P P. 2023a. An overview of hyperspectral image feature extraction， classification methods and the methods based on small samples. Applied Spectroscopy Reviews， 58（6）： 367-400 ［DOI： 10.1080/05704928.2021.1999252http://dx.doi.org/10.1080/05704928.2021.1999252］

Li Y T， Li Z L， Wu H， Zhou C H， Liu X Y， Leng P， Yang P， Wu W B， Tang R L， Shang G F and Ma L L. 2023b. Biophysical impacts of earth greening can substantially mitigate regional land surface temperature warming. Nature Communications， 14（1）： #121 ［DOI： 10.1038/s41467-023-35799-4http://dx.doi.org/10.1038/s41467-023-35799-4］

Li Z L， Tang B H， Wu H， Ren H Z， Yan G J， Wan Z M， Trigo I F and Sobrino J A. 2013. Satellite-derived land surface temperature： current status and perspectives. Remote Sensing of Environment， 131： 14-37 ［DOI： 10.1016/j.rse.2012.12.008http://dx.doi.org/10.1016/j.rse.2012.12.008］

Li Z L， Wu H， Duan S B， Zhao W， Ren H Z， Liu X Y， Leng P， Tang R L， Ye X， Zhu J S， Sun Y W， Si M L， Liu M， Li J H， Zhang X， Shang G F， Tang B H， Yan G J and Zhou C H. 2023c. Satellite remote sensing of global land surface temperature： definition， methods， products， and applications. Reviews of Geophysics， 61（1）： #2022RG000777 ［DOI： 10.1029/2022RG000777http://dx.doi.org/10.1029/2022RG000777］

Liu H Z， Wu K， Xu H G and Xu Y. 2021. Lithology classification using TASI thermal infrared hyperspectral data with convolutional neural networks. Remote Sensing， 13（16）： #3117 ［DOI： 10.3390/rs13163117http://dx.doi.org/10.3390/rs13163117］

Liu K， Su H B and Li X K. 2016. Estimating high-resolution urban surface temperature using a hyperspectral thermal mixing （HTM） approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 9（2）： 804-815 ［DOI： 10.1109/JSTARS.2015.2459375http://dx.doi.org/10.1109/JSTARS.2015.2459375］

Lu X C， Zhang J P， Li T and Zhang G L. 2015. Synergetic classification of long-wave infrared hyperspectral and visible images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 8（7）： 3546-3557 ［DOI： 10.1109/JSTARS.2015.2442594http://dx.doi.org/10.1109/JSTARS.2015.2442594］

Ma C Y， Qian Y G， Li K， Dou X H， Shen H F， Tang H Z， Qiu S， Zhang L H， Jia Y Y and Ou-Yang G Z. 2023. Temperature and emissivity retrieval from hyperspectral thermal infrared data using dictionary-based sparse representation for emissivity. IEEE Transactions on Geoscience and Remote Sensing， 61： #5002016 ［DOI： 10.1109/TGRS.2023.3268860http://dx.doi.org/10.1109/TGRS.2023.3268860］

Manolakis D， Pieper M， Truslow E， Lockwood R， Weisner A， Jacobson J and Cooley T. 2019. Longwave infrared hyperspectral imaging： principles， progress， and challenges. IEEE Geoscience and Remote Sensing Magazine， 7（2）： 72-100 ［DOI： 10.1109/MGRS.2018.2889610http://dx.doi.org/10.1109/MGRS.2018.2889610］

Manolakis D， Siracusa C and Shaw G. 2001. Hyperspectral subpixel target detection using the linear mixing model. IEEE Transactions on Geoscience and Remote Sensing， 39（7）： 1392-1409 ［DOI： 10.1109/36.934072http://dx.doi.org/10.1109/36.934072］

Manolakis D G. 2005. Taxonomy of detection algorithms for hyperspectral imaging applications. Optical Engineering， 44（6）： #066403 ［DOI： 10.1117/1.1930927http://dx.doi.org/10.1117/1.1930927］

Matteoli S， Diani M and Corsini G. 2010. Improved estimation of local background covariance matrix for anomaly detection in hyperspectral images. Optical Engineering， 49（4）： #046201 ［DOI： 10.1117/1.3386069http://dx.doi.org/10.1117/1.3386069］

McElhinney O， Pieper M L， Manolakis D， Loughlin C， Ingle V， Bostick R and Weisner A. 2022. Spline based emissivity retrieval for LWIR hyperspectral imagery//Proceedings of the Algorithms， Technologies， and Applications for Multispectral and Hyperspectral Imaging XXVIII. Orlando， USA： SPIE： 226-236 ［DOI： 10.1117/12.2618553http://dx.doi.org/10.1117/12.2618553］

Meerdink S， Roberts D， Hulley G， Gader P， Pisek J， Adamson K， King J and Hook S J. 2019. Plant species’ spectral emissivity and temperature using the hyperspectral thermal emission spectrometer （HyTES） sensor. Remote Sensing of Environment， 224： 421-435 ［DOI： 10.1016/j.rse.2019.02.009http://dx.doi.org/10.1016/j.rse.2019.02.009］

Miao X Y， Zhang Y， Zhang J P and Zhong S W， 2017. Object parameters optimization on pure and mixed pixels in thermal hyperspectral imagery//Proceedings of the Infrared Remote Sensing and Instrumentation XXV. ［s.l.］： SPIE： 156-163 ［DOI： 10.1117/12.2272521http://dx.doi.org/10.1117/12.2272521］

Mushkin A， Gillespie A R， Abbott E A， Batbaatar J， Hulley G， Tan H， Tratt D M and Buckland K N. 2020. Validation of ASTER emissivity retrieval using the mako airborne TIR imaging spectrometer at the algodones dune field in Southern California， USA. Remote Sensing， 12（5）： #815 ［DOI： 10.3390/rs12050815http://dx.doi.org/10.3390/rs12050815］

O’Keefe D S， Nauyoks S N， Hawks M R， Meola J and Gross K C. 2022. Oblique in-scene atmospheric compensation. IEEE Transactions on Geoscience and Remote Sensing， 60： #5533615 ［DOI： 10.1109/TGRS.2022.3186676http://dx.doi.org/10.1109/TGRS.2022.3186676］

Omruuzun F and Cetin Y Y. 2015. Endmember signature based detection of flammable gases in LWIR hyperspectral images//Proceedings of the Advanced Environmental， Chemical， and Biological Sensing Technologies XII. Baltimore， USA： SPIE： 168-176 ［DOI： 10.1117/12.2182060http://dx.doi.org/10.1117/12.2182060］

Özdemir O B and Koz A. 2023. 3D-CNN and autoencoder-based gas detection in hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 16： 1474-1482 ［DOI： 10.1109/JSTARS.2023.3235781http://dx.doi.org/10.1109/JSTARS.2023.3235781］

Palluconi F D and Meeks G R. 1985. Thermal infrared multispectral scanner （TIMS）： an investigator’s guide to TIMS data ［EB/OL］. ［2023-10-02］. https://ntrs.nasa.gov/citations/19850019974https://ntrs.nasa.gov/citations/19850019974

Pieper M L， McElhinney O， Manolakis D， Bostick R and Weisner A. 2023. Concurrent atmospheric retrieval and wavelength calibration correction technique for improved emissivity retrieval//Proceedings of the Algorithms， Technologies， and Applications for Multispectral and Hyperspectral Imaging XXIX. Orlando， USA： SPIE： #PC1251908 ［DOI： 10.1117/12.2663931］

Pignatti S， Lapenna V， Palombo A， Pascucci S， Pergola N and Cuomo V. 2011. An advanced tool of the CNR IMAA EO facilities： overview of the TASI-600 hyperspectral thermal spectrometer//Proceedings of the 3rd Workshop on Hyperspectral Image and Signal Processing： Evolution in Remote Sensing. Lisbon， Portugal： IEEE： 1-4 ［DOI： 10.1109/WHISPERS.2011.6080890http://dx.doi.org/10.1109/WHISPERS.2011.6080890］

Ramsey M S and Christensen P R. 1998. Mineral abundance determination： Quantitative deconvolution of thermal emission spectra. Journal of Geophysical Research： Solid Earth， 103（B1）： 577-596 ［DOI： 10.1029/97JB02784http://dx.doi.org/10.1029/97JB02784］

Rankin B M， Meola J and Eismann M T. 2017. Spectral radiance modeling and Bayesian model averaging for longwave infrared hyperspectral imagery and subpixel target identification. IEEE Transactions on Geoscience and Remote Sensing， 55（12）： 6726-6735 ［DOI： 10.1109/TGRS.2017.2731955http://dx.doi.org/10.1109/TGRS.2017.2731955］

Reed I S and Yu X. 1990. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustics， Speech， and Signal Processing， 38（10）： 1760-1770 ［DOI： 10.1109/29.60107http://dx.doi.org/10.1109/29.60107］

Ren H Z， Ye X， Nie J， Meng J J， Fan W J， Qin Q M， Liang Y Z and Liu H C. 2022. Retrieval of land surface temperature， emissivity， and atmospheric parameters from hyperspectral thermal infrared image using a feature-band linear-format hybrid algorithm. IEEE Transactions on Geoscience and Remote Sensing， 60： #4401015 ［DOI： 10.1109/TGRS.2020.3047381http://dx.doi.org/10.1109/TGRS.2020.3047381］

Rodarmel C and Shan J. 2002. Principal component analysis for hyperspectral image classification. Surveying and Land Information Science， 62（2）： 115-123

Scafutto R D P M， de Souza Filho C R， Riley D N and de Oliveira W J. 2018. Evaluation of thermal infrared hyperspectral imagery for the detection of onshore methane plumes： significance for hydrocarbon exploration and monitoring. International Journal of Applied Earth Observation and Geoinformation， 64： 311-325 ［DOI： 10.1016/j.jag.2017.07.002http://dx.doi.org/10.1016/j.jag.2017.07.002］

Scafutto R D P M， Lievens C， Hecker C， van der Meer F D and de Souza Filho C R. 2021. Detection of petroleum hydrocarbons in continental areas using airborne hyperspectral thermal infrared data （SEBASS）. Remote Sensing of Environment， 256： #112323 ［DOI： 10.1016/j.rse.2021.112323http://dx.doi.org/10.1016/j.rse.2021.112323］

Schaum A. 2004. Joint subspace detection of hyperspectral targets//Proceedings of IEEE Aerospace Conference Proceedings. SkyBig， USA： IEEE： #1824 ［DOI： 10.1109/AERO.2004.1367963http://dx.doi.org/10.1109/AERO.2004.1367963］

Shao H L， Liu C Y， Xie F， Li C L and Wang J Y. 2020. Noise-sensitivity analysis and improvement of automatic retrieval of temperature and emissivity using spectral smoothness. Remote Sensing， 12（14）： #2295 ［DOI： 10.3390/rs12142295http://dx.doi.org/10.3390/rs12142295］

Smith M D， Bandfield J L and Christensen P R. 2000. Separation of atmospheric and surface spectral features in mars global surveyor thermal emission spectrometer （TES） spectra. Journal of Geophysical Research： Planets， 105（E4）： 9589-9607 ［DOI： 10.1029/1999JE001105http://dx.doi.org/10.1029/1999JE001105］

Sundberg R， Adler-Golden S and Conforti P. 2015. Long-wavelength infrared hyperspectral data “mining” at cuprite， NV//Proceedings of the Imaging Spectrometry XX. San Diego， USA： SPIE： 12-18 ［DOI： 10.1117/12.2187061http://dx.doi.org/10.1117/12.2187061］

Tarabalka Y， Fauvel M， Chanussot J and Benediktsson J A. 2010. SVM- and MRF-based method for accurate classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters， 7（4）： 736-740 ［DOI： 10.1109/LGRS.2010.2047711http://dx.doi.org/10.1109/LGRS.2010.2047711］

Tellier Y， Crevoisier C， Armante R， Dufresne J L and Meilhac N. 2022. Computation of longwave radiative flux and vertical heating rate with 4A-Flux v1.0 as an integral part of the radiative transfer Code 4A/OP v1.5. Geoscientific Model Development， 15（13）： 5211-5231 ［DOI： 10.5194/gmd-15-5211-2022http://dx.doi.org/10.5194/gmd-15-5211-2022］

Thai B and Healey G. 2002. Invariant subpixel material detection in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing， 40（3）： 599-608 ［DOI： 10.1109/TGRS.2002.1000320http://dx.doi.org/10.1109/TGRS.2002.1000320］

Theiler J and Wohlberg B. 2013. Detection of unknown gas-phase chemical plumes in hyperspectral imagery//Proceedings of the Algorithms and Technologies for Multispectral， Hyperspectral， and Ultraspectral Imagery XIX. Baltimore， USA： SPIE： 346-357 ［DOI： 10.1117/12.2016211http://dx.doi.org/10.1117/12.2016211］

Tong Q X， Xue Y Q and Zhang L F. 2014. Progress in hyperspectral remote sensing science and technology in China over the past three decades. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 7（1）： 70-91 ［DOI： 10.1109/JSTARS.2013.2267204http://dx.doi.org/10.1109/JSTARS.2013.2267204］

Truslow E， Manolakis D， Cooley T and Meola J. 2016. Statistical modeling of natural backgrounds in hyperspectral LWIR data//Proceedings of the Imaging Spectrometry XXI. San Diego， USA： SPIE： #99760H ［DOI： 10.1117/12.2239432http://dx.doi.org/10.1117/12.2239432］

Vaughan R G， Calvin W M and Taranik J V. 2003. SEBASS hyperspectral thermal infrared data： surface emissivity measurement and mineral mapping. Remote Sensing of Environment， 85（1）： 48-63 ［DOI： 10.1016/S0034-4257（02）00186-4http://dx.doi.org/10.1016/S0034-4257（02）00186-4］

Veraverbeke S， Dennison P， Gitas I， Hulley G， Kalashnikova O， Katagis T， Kuai L， Meng R， Roberts D and Stavros N. 2018. Hyperspectral remote sensing of fire： state-of-the-art and future perspectives. Remote Sensing of Environment， 216： 105-121 ［DOI： 10.1016/j.rse.2018.06.020http://dx.doi.org/10.1016/j.rse.2018.06.020］

Vidal C and Pasquini C. 2021. A comprehensive and fast microplastics identification based on near-infrared hyperspectral imaging （HSI-NIR） and chemometrics. Environmental Pollution， 285： #117251 ［DOI： 10.1016/j.envpol.2021.117251http://dx.doi.org/10.1016/j.envpol.2021.117251］

Villa A， Benediktsson J A， Chanussot J and Jutten C. 2011. Hyperspectral image classification with independent component discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing， 49（12）： 4865-4876 ［DOI： 10.1109/TGRS.2011.2153861http://dx.doi.org/10.1109/TGRS.2011.2153861］

Wan Z M and Li Z L. 1997. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Transactions on Geoscience and Remote Sensing， 35（4）： 980-996 ［DOI： 10.1109/36.602541http://dx.doi.org/10.1109/36.602541］

Wang H， Mao K B， Yuan Z J， Shi J C， Cao M M， Qin Z H， Duan S B and Tang B H. 2021. A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning. Remote Sensing of Environment， 265： #112665 ［DOI： 10.1016/j.rse.2021.112665http://dx.doi.org/10.1016/j.rse.2021.112665］

Wang J Y， Li C L， Ji H Z， Yuan L Y， Lyu G， Liu E G and Wang Y M. 2015. Status and prospect of thermal infrared hyperspectral imaging technology. Journal of Infrared and Millimeter Waves， 34（1）： 51-59

王建宇，李春来，姬弘桢，袁立银，吕刚，刘恩光，王跃明. 2015. 热红外高光谱成像技术的研究现状与展望. 红外与毫米波学报， 34（1）： 51-59 ［DOI： 10.11972/j.issn.1001-9014.2015.01.010http://dx.doi.org/10.11972/j.issn.1001-9014.2015.01.010］

Wang N， Wu H， Nerry F， Li C R and Li Z L. 2011. Temperature and emissivity retrievals from hyperspectral thermal infrared data using linear spectral emissivity constraint. IEEE Transactions on Geoscience and Remote Sensing， 49（4）： 1291-1303 ［DOI： 10.1109/TGRS.2010.2062527http://dx.doi.org/10.1109/TGRS.2010.2062527］

Wang S Y， Wang X Y， Zhong Y F and Zhang L P. 2020. Hyperspectral anomaly detection via locally enhanced low-rank prior. IEEE Transactions on Geoscience and Remote Sensing， 58（10）： 6995-7009 ［DOI： 10.1109/TGRS.2020.2978510http://dx.doi.org/10.1109/TGRS.2020.2978510］

Wang X Y， Zhong Y F， Zhang L P and Xu Y Y. 2017. Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing， 55（11）： 6287-6304 ［DOI： 10.1109/TGRS.2017.2724944http://dx.doi.org/10.1109/TGRS.2017.2724944］

Weng Q H. 2009. Thermal infrared remote sensing for urban climate and environmental studies： methods， applications， and trends. ISPRS Journal of Photogrammetry and Remote Sensing， 64（4）： 335-344 ［DOI： 10.1016/j.isprsjprs.2009.03.007http://dx.doi.org/10.1016/j.isprsjprs.2009.03.007］

Westing N M， Borghetti B J， Gross K C and Martin J A. 2019. Analysis of long-wave Infrared hyperspectral classification performance across changing scene illumination//Proceedings of the Algorithms， Technologies， and Applications for Multispectral and Hyperspectral Imagery XXV. Baltimore， USA： SPIE： #109860V ［DOI： 10.1117/12.2518109http://dx.doi.org/10.1117/12.2518109］

Wu H， Li X J， Li Z L， Duan S B and Qian Y G. 2021. Hyperspectral thermal infrared remote sensing： current status and perspectives. National Remote Sensing Bulletin， 25（8）： 1567-1590

吴骅，李秀娟，李召良，段四波，钱永刚. 2021. 高光谱热红外遥感：现状与展望. 遥感学报， 25（8）： 1567-1590 ［DOI： 10.11834/jrs.20211306http://dx.doi.org/10.11834/jrs.20211306］

Ye X， Ren H Z， Nie J， Hui J， Jiang C C， Zhu J S， Fan W J， Qian Y G and Liang Y Z. 2022. Simultaneous estimation of land surface and atmospheric parameters from thermal hyperspectral data using a LSTM-CNN combined deep neural network. IEEE Geoscience and Remote Sensing Letters， 19： #5508705 ［DOI： 10.1109/LGRS.2021.3104501http://dx.doi.org/10.1109/LGRS.2021.3104501］

Young S J， Johnson B R and Hackwell J A. 2002. An in-scene method for atmospheric compensation of thermal hyperspectral data. Journal of Geophysical Research： Atmospheres， 107（D24）： #4774 ［DOI： 10.1029/2001JD001266http://dx.doi.org/10.1029/2001JD001266］

Zelinski M E. 2018. Overhead longwave infrared hyperspectral material identification using radiometric models. Journal of Applied Remote Sensing， 12（2）： #025019 ［DOI： 10.1117/1.JRS.12.025019http://dx.doi.org/10.1117/1.JRS.12.025019］

Zelinski M E. 2020. Off-nadir longwave infrared hyperspectral material identification using radiometric models//IGARSS， 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa， USA： IEEE： 3963-3966 ［DOI： 10.1109/IGARSS39084.2020.9324589http://dx.doi.org/10.1109/IGARSS39084.2020.9324589］

Zhang B. 2017. Current status and future prospects of remote sensing. Bulletin of Chinese Academy of Sciences， 32（7）： 774-784

张兵. 2017. 当代遥感科技发展的现状与未来展望. 中国科学院院刊， 32（7）： 774-784 ［DOI： 10.16418/j.issn.1000-3045.2017.07.012http://dx.doi.org/10.16418/j.issn.1000-3045.2017.07.012］

Zhang L F， Zhang L P， Tao D C and Huang X. 2012. On combining multiple features for hyperspectral remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing， 50（3）： 879-893 ［DOI： 10.1109/TGRS.2011.2162339http://dx.doi.org/10.1109/TGRS.2011.2162339］

Zhang Y Z， Wu H， Jiang X G， Jiang Y Z， Liu Z X and Nerry F. 2017. Land surface temperature and emissivity retrieval from field-measured hyperspectral thermal infrared data using wavelet transform. Remote Sensing， 9（5）： #454 ［DOI： 10.3390/rs9050454http://dx.doi.org/10.3390/rs9050454］

Zhang Y X， Du B， Zhang L P and Wang S. 2015. A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing， 54（3）： 1376-1389［DOI： 10.1109/TGRS.2015.2479299http://dx.doi.org/10.1109/TGRS.2015.2479299］

Zhao Y Q， Jin Z， Zhang F， Zhao H Y， Tao Z W， Dou C F， Xu X H and Liu D H. 2023. Deep-learning-based image captioning： analysis and prospects. Journal of Image and Graphics， 28（9）： 2788-2816

赵永强，金芝，张峰，赵海燕，陶政为，豆乘风，徐新海，刘东红. 2023. 深度学习图像描述方法分析与展望. 中国图象图形学报， 28（9）： 2788-2816 ［DOI： 10.11834/jig.220660http://dx.doi.org/10.11834/jig.220660］

Zheng Z， Zhong Y F， Ma A L and Zhang L P. 2020. FPGA： fast patch-free global learning framework for fully end-to-end hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing， 58（8）： 5612-5626 ［DOI： 10.1109/TGRS.2020.2967821http://dx.doi.org/10.1109/TGRS.2020.2967821］

Zhong Y F， Hu X， Luo C， Wang X Y， Zhao J and Zhang L P. 2020. WHU-Hi： UAV-borne hyperspectral with high spatial resolution （H2） benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF. Remote Sensing of Environment， 250： #112012 ［DOI： 10.1016/j.rse.2020.112012http://dx.doi.org/10.1016/j.rse.2020.112012］

Zhong Y F， Wang X Y， H X， Wang S Y， Wan Y T， Tang G and Zhang L P. 2023. Hyperspectral with high-spatial resolution remote sensing from observation， processing to applications. Acta Geodaetica et Cartographica Sinica， 52（7）： 1212-1226

钟燕飞，王心宇，胡鑫，王少宇，万瑜廷，唐舸，张良培. 2023. 高光谱高空间分辨率遥感观测、处理与应用. 测绘学报， 52（7）： 1212-1226 ［DOI： 10.11947/j.AGCS.2023.20220715http://dx.doi.org/10.11947/j.AGCS.2023.20220715］

Zhou S G and Cheng J. 2018. A multi-scale wavelet-based temperature and emissivity separation algorithm for hyperspectral thermal infrared data. International Journal of Remote Sensing， 39（22）： 8092-8112 ［DOI： 10.1080/01431161.2018.1482019http://dx.doi.org/10.1080/01431161.2018.1482019］

Zhu X H. 2021. Research on Thermal Infrared Hyperspectral Anomaly Detection Method Based on Low-Rank Prior. Wuhan， China： Wuhan University

朱绪鹤. 2021. 基于低秩先验的热红外高光谱异常目标探测方法研究. 武汉：武汉大学［DOI： 10.27379/d.cnki.gwhdu.2021.000070http://dx.doi.org/10.27379/d.cnki.gwhdu.2021.000070］

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