航天高光谱遥感应用研究进展(特邀)
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摘要
近年来随着高光谱成像技术的快速发展,航天高光谱遥感数据在各领域应用研究中取得了良好的发展与突破。文中回顾了国内外航天高光谱成像技术的发展历程,介绍了有代表性的航天高光谱成像仪的主要应用技术指标,较为系统地总结和分析了近五年来航天高光谱遥感数据在国土资源、农林遥感、海洋湖泊遥感、城市环境、灾害监测及其他方面等各领域的最新应用研究进展。对基于AI技术的高光谱信息提取与应用、基于高光谱遥感的多源数据融合与应用以及面向深空探测领域的高光谱数据分析与应用等发展趋势做了展望,未来航天高光谱成像仪技术的进一步突破和应用研究需求的牵引将会推动高光谱应用领域更大范围的创新与发展。
With the rapid development of hyperspectral imaging technology, space hyperspectral remote sensing data have been successfully applied to various fields in recent years. The development of space hyperspectral imaging technology at home and abroad was reviewed, the technical standards of representative space hyperspectral imagers were introduced. The latest applications of hyperspectral data in land resources, agriculture and forestry, ocean and lake remote sensing, urban environment, disaster monitoring and other fields in the past five years were systematically summarized and analysed. The outlook of future hyperspectral remote sensing was provided including hyperspectral information extraction and application based on AI technology, the multi-source data fusion and applications, and the analysis and application of hyperspectral data for deep space exploration. Further developments of space hyperspectral imager technology driven by applications will promote the innovated use of hyperspectral data in a wider range of fields.
With the rapid development of hyperspectral imaging technology, space hyperspectral remote sensing data have been successfully applied to various fields in recent years. The development of space hyperspectral imaging technology at home and abroad was reviewed, the technical standards of representative space hyperspectral imagers were introduced. The latest applications of hyperspectral data in land resources, agriculture and forestry, ocean and lake remote sensing, urban environment, disaster monitoring and other fields in the past five years were systematically summarized and analysed. The outlook of future hyperspectral remote sensing was provided including hyperspectral information extraction and application based on AI technology, the multi-source data fusion and applications, and the analysis and application of hyperspectral data for deep space exploration. Further developments of space hyperspectral imager technology driven by applications will promote the innovated use of hyperspectral data in a wider range of fields.
引文
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[2] Barnsley M J, Settle J J, Cutter M A, et al. The PROBA/CHRIS mission:A low-cost smallsat for hyperspectral multiangle observations of the earth surface and atmosphere[J]. IEEE Transactions on Geoscience and Remote Sensing,2004, 42(7):1512-1520.
[3] Yuan Yan, Li Liying, Xiong Wang′e, et al. Mechanical structure design for hyperspectral imager of HJ-1A satellite[J]. Spacecraft Engineering, 2009, 18(6):97-105.
[4] Lucke R L, Corson M, McGlothlin N R, et al. Hyperspectral imager for the coastal ocean:instrument description and first images[J]. Applied Optics, 2011, 50(11):1501-1516.
[5] Gao Ming, Zhang Shancong, Li Shengyang. Tiangong-1hyperspectral remote sensing and application[J]. Journal of Remote Sensing, 2014, 18(s1):2-10.(in Chinese)
[6] Liu Yinnian. Visible-shortwave infrared hyperspectral imager of GF-5 satellite[J]. Spacecraft Recovery&Remote Sengsing, 2018, 39(3):25-28.(in Chinese)
[7] Murchie S, Arvidson R, Bedini P, et al. Compact Reconnaissance Imaging Spectrometer for Mars(CRISM)on Mars Reconnaissance Orbiter(MRO)[J]. Journal of Geophysical Research Atmospheres, 2007, 112(E5):431-433.
[8] Li Xiaopeng, Chen Jianpeng, Wang Xiang. Inversion of lunar nearside FeO and Al2O3based on Chang′e-1 reflectance data[J]. China Mining Magazine, 2018, 27(7):150-156.(in Chinese)
[9] Staenz K, Held A. Summary of current and future terrestrial civilian hyperspectral spaceborne systems[C]//2012 IEEE International Geoscience and Remote Sensing Symposium,2012:123-126.
[10] Stefano P, Angelo P, Simone P, et al. The PRISMA hyperspectral mission:science activities and opportunities for agriculture and land monitoring[C]//2013 IEEE International Geoscience and Remote Sensing Symposium, 2013:4558-4561.
[11] Guanter L, Kaufmann H, Segl K, et al. The EnMAP spaceborne imaging spectroscopy mission for earth observation[J]. Remote Sensing, 2015, 7(7):8830-8857.
[12] Lee C M, Cable M L, Hook S J, et al. An introduction to the NASA Hyperspectral InfraRed Imager(HyspIRI)mission and preparatory activities[J]. Remote Sensing of Environment, 2015, 167:6-19.
[13] Lefevre-Fonollosa M-J, Fratter I, Mandea M. HYPXIM:an innovative hyperspectral satellite for science, security and defence[J]. Egu General Assembly, 2013, 15:10129.
[14] Chen S, Huang S, Liu Y, et al. Soil and vegetation spectral coupling difference(SVSCD)for minerals extraction from hyperion data in vegetation covered area[J]. Chinese Geographical Science, 2018, 28(6):957-972.
[15] Lei L, Feng J, Rivard B, et al. Mapping alteration using imagery from the Tiangong-1 hyperspectral spaceborne system:Example for the Jintanzi gold province, China[J].International Journal of Applied Earth Observation&Geoinformation, 2017, 64:31-41.
[16] Lin Jian, Yan Baikun, Dong Xinfeng, et al. Evaluating of Tiangong-1 imaging spectrometer data oriented to geological applications[J]. Journal of Remote Sensing, 2014, 18(s1):74-83.(in Chinese)
[17] Elatawneh A, Kalaitzidis C, Petropoulos G P, et al.Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data[J]. International Journal of Digital Earth, 2014, 7(3):194-216.
[18] Xing C, Ma L, Yang X Q. Stacked denoise autoencoder based feature extraction and classification for hyperspectral images[J]. Journal of Sensors, 2016, 2016:3632943.
[19] Li Zhuqiang, Zhu Ruifei, Gao Fang, et al. Hyperspectral remote sensing image classification based on threedimensional convolution neural network combined with conditional random field optimization[J]. Acta Optica Sinica, 2018, 38(8):404-413.(in Chinese)
[20] Chen B, Huang B, Xu B. Multi-source remotely sensed data fusion for improving land cover classification[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2017,124:27-39.
[21] Chen Bin, Huang Bo, Xu Bing. Multi-source remotely sensed data fusion for improving land cover classification[J]. Isprs Journal of Photogrammetry&Remote Sensing, 2017, 124:27-39.
[22] Lu P, Wang L, Niu Z, et al. Prediction of soil properties using laboratory VIS-NIR spectroscopy and Hyperion imagery[J]. Journal of Geochemical Exploration, 2013,132:26-33.
[23] Steinberg A, Chabrillat S, Stevens A, et al. Prediction of common surface soil properties based on Vis-NIR airborne and simulated EnMAP imaging spectroscopy data:prediction accuracy and influence of spatial resolution[J]. Remote Sensing, 2016, 8(8):613.
[24] Castaldi F, Palombo A, Santini F, et al. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon[J]. Remote Sensing of Environment, 2016, 179:54-65.
[25] Malec S, Rogge D, Heiden U, et al. Capability of spaceborne hyperspectral EnMAP mission for mapping fractional cover for soil erosion modeling[J].Remote Sensing,2015, 7(9):11776-11800.
[26] Liang Liang, Di Liping, Zhang Lianpeng, et al. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method[J]. Remote Sensing of Environment, 2015, 165:123-134.
[27] Heiskanen J, Rautiainen M, Stenberg P, et al. Sensitivity of narrowband vegetation indices to boreal forest LAI,reflectance seasonality and species composition[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2013, 78:1-14.
[28] Ali I, Greifeneder F, Stamenkovic J, et al. Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data[J]. Remote Sensing,2015, 7(12):16398-16421.
[29] Kattenborn T, Maack J, Fassnacht F, et al. Mapping forest biomass from space-Fusion of hyperspectral EO1-hyperion data and Tandem-X and WorldView-2 canopy height models[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 35:359-367.
[30] Marshall M, Thenkabail P. Advantage of hyperspectral EO-1Hyperion over multispectral IKONOS, GeoEye-1,WorldView-2, Landsat ETM plus, and MODIS vegetation indices in crop biomass estimation[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2015, 108:205-218.
[31] Xu Jin, Meng Jihua. Overview on estimating crop chlorophyll content with remote sensing[J]. Remote Sensing Technology and Application, 2016, 31(1):74-85.(in Chinese)
[32] Liang L, Qin Z H, Zhao S H, et al. Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method[J]. International Journal of Remote Sensing, 2016, 37(13):2923-2949.
[33] Yang X G, Yu Y, Fan W Y. Chlorophyll content retrieval from hyperspectral remote sensing imagery[J]. Environmental Monitoring and Assessment, 2015, 187(7):13.
[34] Delloye C, Weiss M, Defourny P. Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems[J]. Remote Sensing of Environment, 2018, 216:245-261.
[35] Stagakis S, Markos N, Sykioti O, et al. Tracking seasonal changes of leaf and canopy light use efficiency in a Phlomis fruticosa Mediterranean ecosystem using field measurements and multi-angular satellite hyperspectral imagery[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2014, 97:138-151.
[36] Castaldi F, Castrignano A, Casa R. A data fusion and spatial data analysis approach for the estimation of wheat grain nitrogen uptake from satellite data[J]. International Journal of Remote Sensing, 2016, 37(18):4317-4336.
[37] Jia Kun, Li Qiangzi. Review of features selection in crop classification using remote sensing data[J]. Resources Science, 2013, 35(12):2507-2516.(in Chinese)
[38] Thenkabail P S, Mariotto I, Gumma M K, et al. Selection of hyperspectral narrowbands(HNBs)and composition of hyperspectral twoband vegetation indices(HVIs)for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):427-439.
[39] Pan Z K, Huang J F, Wang F M. Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area[J]. International Journal of Applied Earth Observation and Geoinformation, 2013, 25:21-29.
[40] Awad M M. Forest mapping:a comparison between hyperspectral and multispectral images and technologies[J].Journal of Forestry Research, 2018, 29(5):1395-1405.
[41] Zhang Kang, Hei Baoqin, Zhou Zhuang, et al. CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification[J]. Journal of Remote Sensing, 2018, 22(1):87-96.(in Chinese)
[42] Mariotto I, Thenkabail P S, Huete A, et al. Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission[J]. Remote Sensing of Environment, 2013, 139:291-305.
[43] Zhang Q, Middleton E M, Cheng Y B, et al. Integrating chlorophyll fAPAR and nadir photochemical reflectance index from EO-1/Hyperion to predict cornfield daily gross primary production[J]. Remote Sensing of Environment, 2016, 186:311-321.
[44] Deng Shiquan, Tian Liqiao, Li Jian, et al. A novel chlorophyll-a inversion model in turbid water for GF-5satellite hyperspectral sensordir photochemical refle[J].Journal of Huazhong Normal University(Natural Sciences),2018, 52(3):409-415.(in Chinese)
[45] Pan Banglong, Yi Weining, Wang Xianhua, et al.Geostatistics algorithm design on hyperspectral inversion of total phosphorus of lake[J]. Infrared and Laser Engineering,2012, 41(5):1255-1260.(in Chinese)
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