Advances in spaceborne hyperspectral remote sensing in China

References

• Acito, N., M. Diani, and G. Corsini. 2009. “Hyperspectral Signal Subspace Identification in the Presence of Rare Signal Components.” IEEE Transactions on Geoscience and Remote Sensing 48 (4): 1940–1954. doi:10.1109/TGRS.2009.2035445.
   Web of Science ®Google Scholar
• Adam, E., O. Mutanga, and D. Rugege. 2010. “Multispectral and Hyperspectral Remote Sensing for Identification and Mapping of Wetland Vegetation: A Review.” Wetlands Ecology and Management 18 (3): 281–296. doi:10.1007/s11273-009-9169-z.
   Web of Science ®Google Scholar
• Alam, F. I., J. Zhou, A. W.-C. Liew, X. Jia, J. Chanussot, and Y. Gao. 2017. “Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation.” IEEE Transactions on Geoscience and Remote Sensing 57 (3): 1612–1628. doi:10.1109/TGRS.2018.2867679.
   Web of Science ®Google Scholar
• Altmann, Y., A. Halimi, N. Dobigeon, and J. Y. Tourneret. 2012. “Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery.” IEEE Transactions on Image Processing 21 (6, June): 3017–3025. doi:10.1109/TIP.2012.2187668.
   PubMed Web of Science ®Google Scholar
• Babey, S., and C. Anger. 1989. “A Compact Airborne Spectrographic Imager (CASI).” Quantitative Remote Sensing: An Economic Tool for the Nineties 1: 1028–1031.
   Google Scholar
• Bayliss, J. D., J. A. Gualtieri, and R. F. Cromp. 1998. “Analyzing Hyperspectral Data with Independent Component Analysis.” 26th AIPR Workshop: Exploiting New Image Sources and Sensors, no. 3240: 133–143.
   Google Scholar
• Benediktsson, J. A., P. H. Swain, and O. K. Ersoy. 1990. “Neural Network Approaches versus Statistical-methods in Classification of Multisource Remote-sensing Data.” IEEE Transactions on Geoscience and Remote Sensing 28 (4): 540–552. doi:10.1109/TGRS.1990.572944.
   Web of Science ®Google Scholar
• Bioucas-Dias, J. M., A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. J. Chanussot. 2012. “Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-based Approaches.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (2): 354–379. doi:10.1109/JSTARS.2012.2194696.
   Web of Science ®Google Scholar
• Boardman, J. W., F. A. Kruse, and R. O. Green. 1995. Mapping Target Signatures via Partial Unmixing of AVIRIS Data, Jet Propulsion Laboratory.
   Google Scholar
• Candes, E. J., X. Li, Y. Ma, and J. Wright. 2011. “Robust Principal Component Analysis?” Journal of the ACM 58 (3): 1–37. doi:10.1145/1970392.1970395.
   Web of Science ®Google Scholar
• Cao, Y., Y. Ye, H. Zhao, Y. Jiang, H. Wang, Y. Shang, and J. Wang. 2018. “Remote Sensing of Water Quality Based on HJ-1A HSI Imagery with Modified Discrete Binary Particle Swarm Optimization-partial Least Squares (MDBPSO-PLS) in Inland Waters: A Case in Weishan Lake.” Ecological Informatics 44: 21–32. doi:10.1016/j.ecoinf.2018.01.004.
   Web of Science ®Google Scholar
• Chan, T.-H., C.-Y. Chi, Y.-M. Huang, and W.-K. Ma. 2009. “A Convex Analysis-based Minimum-volume Enclosing Simplex Algorithm for Hyperspectral Unmixing.” IEEE Transactions on Signal Processing 57 (11): 4418–4432. doi:10.1109/TSP.2009.2025802.
   Web of Science ®Google Scholar
• Chandrasekhar, S. Massachusetts, 2013. Radiative Transfer. Courier Corporation.
   Google Scholar
• Chang, C. I., and Q. Du. 2004. “Estimation of Number of Spectrally Distinct Signal Sources in Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 42 (3): 608–619. doi:10.1109/TGRS.2003.819189.
   Web of Science ®Google Scholar
• Chang, C.–I. New Jersey, 2007. Hyperspectral Data Exploitation: Theory and Applications. John Wiley & Sons.
   Google Scholar
• Chang, S., B. Du, and L. Zhang. Yokohama, 2019. “A Sparse Autoencoder Based Hyperspectral Anomaly Detection Algorithm Using Residual of Reconstruction Error,” in IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 5488–5491.
   Google Scholar
• Chang, Y., L. Yan, H. Fang, S. Zhong, and W. Liao. 2018. “HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural Network.” IEEE Transactions on Geoscience and Remote Sensing 57 (2): 667–682.
   Web of Science ®Google Scholar
• Chen, B., Z. Wu, J. Wang, J. Dong, L. Guan, J. Chen, K. Yang, and G. Xie. 2015. “Spatio-temporal Prediction of Leaf Area Index of Rubber Plantation Using HJ-1A/1B CCD Images and Recurrent Neural Network.” ISPRS Journal of Photogrammetry and Remote Sensing 102: 148–160. doi:10.1016/j.isprsjprs.2014.12.011.
   Web of Science ®Google Scholar
• Chen, J., T. Cui, Z. Qiu, and C. Lin. 2014a. “A Three-band Semi-analytical Model for Deriving Total Suspended Sediment Concentration from HJ-1A/CCD Data in Turbid Coastal Waters.” ISPRS Journal of Photogrammetry and Remote Sensing 93: 1–13. doi:10.1016/j.isprsjprs.2014.02.011.
   Web of Science ®Google Scholar
• Chen, J., Y. Shao, H. Guo, W. Wang, and B. Zhu. 2003. “Destriping CMODIS Data by Power Filtering.” IEEE Transactions on Geoscience and Remote Sensing 41 (9): 2119–2124. doi:10.1109/TGRS.2003.817206.
   Web of Science ®Google Scholar
• Chen, X., Y. Liu, D. Yang, J. Zhou, C. Guo, Y. Zhang, and Z. Cai. 2018. “Exploration of the Potential for Using Spark-I Observations to Derive Atmospheric Parameters.” International Journal of Remote Sensing 39 (6): 1853–1870. doi:10.1080/01431161.2017.1416694.
   Web of Science ®Google Scholar
• Chen, Y., N. M. Nasrabadi, and T. D. Tran. 2011a. “Sparse Representation for Target Detection in Hyperspectral Imagery.” IEEE Journal of Selected Topics in Signal Processing 5 (3): 629–640. doi:10.1109/JSTSP.2011.2113170.
   Web of Science ®Google Scholar
• Chen, Y., N. M. Nasrabadi, and T. D. Tran. 2011b. “Hyperspectral Image Classification Using Dictionary-based Sparse Representation.” IEEE Transactions on Geoscience and Remote Sensing 49 (10): 3973–3985. doi:10.1109/TGRS.2011.2129595.
   Web of Science ®Google Scholar
• Chen, Y., N. M. Nasrabadi, and T. D. Tran. 2011c. “Simultaneous Joint Sparsity Model for Target Detection in Hyperspectral Imagery.” IEEE Geoscience and Remote Sensing Letters 8 (4): 676–680. doi:10.1109/LGRS.2010.2099640.
   Web of Science ®Google Scholar
• Chen, Y., S.-B. Duan, H. Ren, J. Labed, and Z.-L. Li. 2017. “Algorithm Development for Land Surface Temperature Retrieval: Application to Chinese Gaofen-5 Data.” Remote Sensing 9 (2): 1–19.
   Web of Science ®Google Scholar
• Chen, Y., X. Zhao, and X. Jia. 2015. “Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (6): 2381–2392. doi:10.1109/JSTARS.2015.2388577.
   Web of Science ®Google Scholar
• Chen, Y., Z. Lin, X. Zhao, G. Wang, and Y. Gu. 2014b. “Deep Learning-Based Classification of Hyperspectral Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2094–2107. doi:10.1109/JSTARS.2014.2329330.
   Web of Science ®Google Scholar
• Cheng, L., J. Tao, P. Valks, C. Yu, S. Liu, Y. Wang, X. Xiong, Z. Wang, and L. Chen. 2019. “NO2 Retrieval from the Environmental Trace Gases Monitoring Instrument (EMI): Preliminary Results and Intercomparison with OMI and TROPOMI.” Remote Sensing 11 (24): 1–18. doi:10.3390/rs11243017.
   Web of Science ®Google Scholar
• Cheriyadat, A. M. Mississippi, 2003. Limitations of Principal Component Analysis for Dimensionality-reduction for Classification of Hyperspectral Data. Mississippi State University America.
   Google Scholar
• Chong, K., K. Kanniah, C. Pohl and K. Tan. 2017. “A Review of Remote Sensing Applications for Oil Palm Studies.” Geo-spatial Information Science 20(2): 184-200, DOI: 10.1080/10095020.2017.1337317.
   Google Scholar
• Cocks, T., R. Jenssen, A. Stewart, I. Wilson, and T. Shields. Zurich, 1998. “The HyMapTM Airborne Hyperspectral Sensor: The System, Calibration and Performance.” Proceedings 1805 of the 1st EARSeL workshop on Imaging Spectroscopy, pp. 37–42.
   Google Scholar
• DATTA, D., and S. DEB. 2012. “Analysis of Coastal Land Use/Land Cover Changes in the Indian Sunderbans Using Remotely Sensed Data.” Geo-spatial Information Science 15(4): 241-250, doi: 10.1080/10095020.2012.714104.
   Google Scholar
• Dash, J., and P. J. Curran. 2004. “The MERIS Terrestrial Chlorophyll Index.” International Journal of Remote Sensing 25 (23): 5403–5413. doi:10.1080/0143116042000274015.
   Web of Science ®Google Scholar
• Deng, S., L. Tian, L. Jian, X. Chen, Z. Sun, L. Zhang, and A. Wei. 2018. “A Novel Chlorophyll–A Inversion Model in Turbid Water for GF–5 Satellite Hyperspectral Sensor – A Case in Poyang Lake.” Central China Normal University 52 (3): 409–415.
   Google Scholar
• Di, W., Q. Pan, L. He, and Y. Cheng. 2008. “Anomaly Detection in Hyperspectral Imagery by Fuzzy Integral Fusion of Band-subsets.” Photogrammetric Engineering and Remote Sensing 74 (2): 201–213. doi:10.14358/PERS.74.2.201.
   Web of Science ®Google Scholar
• Dinguirard, M., and P. N. Slater. 1999. “Calibration of Space-Multispectral Imaging Sensors: A Review.” Remote Sensing of Environment 68 (3): 194–205. doi:10.1016/S0034-4257(98)00111-4.
   Web of Science ®Google Scholar
• Dobigeon, N., J.-Y. Tourneret, C. Richard, J. C. M. Bermudez, S. McLaughlin, and A. O. Hero. 2013. “Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms.” IEEE Signal Processing Magazine 31 (1): 82–94.
   Web of Science ®Google Scholar
• Donoho, D. L. 2006. “Compressed Sensing.” IEEE Transactions on Information Theory 52 (4): 1289–1306. doi:10.1109/TIT.2006.871582.
   Web of Science ®Google Scholar
• Du, B., and L. Zhang. 2011. “Random-selection-based Anomaly Detector for Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing 49 (5): 1578–1589. doi:10.1109/TGRS.2010.2081677.
   Web of Science ®Google Scholar
• Elvidge, C. D., and Z. K. Chen. 1995. “Comparison of Broad-band and Narrow-band Red and Near-infrared Vegetation Indexes.” Remote Sensing of Environment 54 (1): 38–48. doi:10.1016/0034-4257(95)00132-K.
   Web of Science ®Google Scholar
• Fan, H., C. Li, Y. Guo, G. Kuang, and J. Ma. 2018. “Spatial–spectral Total Variation Regularized Low-rank Tensor Decomposition for Hyperspectral Image Denoising.” IEEE Transactions on Geoscience and Remote Sensing 56 (10): 6196–6213.
   Web of Science ®Google Scholar
• Fan, W., B. Hu, J. Miller, and M. Li. 2009. “Comparative Study between a New Nonlinear Model and Common Linear Model for Analysing Laboratory Simulated‐forest Hyperspectral Data.” International Journal of Remote Sensing 30 (11): 2951–2962. doi:10.1080/01431160802558659.
   Web of Science ®Google Scholar
• Feng, Y.-Z., and D.-W. Sun. 2012. “Application of Hyperspectral Imaging in Food Safety Inspection and Control: A Review.” Critical Reviews in Food Science and Nutrition 52 (11): 1039–1058. doi:10.1080/10408398.2011.651542.
   PubMed Web of Science ®Google Scholar
• Gadallah, F. L., F. Csillag, and E. J. M. Smith. 2000. “Destriping Multisensor Imagery with Moment Matching.” International Journal of Remote Sensing 21 (12): 2505–2511. doi:10.1080/01431160050030592.
   Web of Science ®Google Scholar
• Galvão, L. S., A. R. Formaggio, E. G. Couto, and D. A. Roberts. 2008. “Relationships between the Mineralogical and Chemical Composition of Tropical Soils and Topography from Hyperspectral Remote Sensing Data.” ISPRS Journal of Photogrammetry and Remote Sensing 63 (2): 259–271. doi:10.1016/j.isprsjprs.2007.09.006.
   Web of Science ®Google Scholar
• Ghamisi, P., J. Plaza, Y. Chen, J. Li, and A. Plaza. 2017a. “Advanced Spectral Classifiers for Hyperspectral Images A Review.” IEEE Geoscience and Remote Sensing Magazine 5 (1): 8–32. doi:10.1109/MGRS.2016.2616418.
   Web of Science ®Google Scholar
• Ghamisi, P., N. Yokoya, J. Li, W. Liao, S. Liu, J. Plaza, B. Rasti, et al. 2017b. “Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art.” IEEE Geoscience and Remote Sensing Magazine 5 (4): 37–78. DOI:10.1109/MGRS.2017.2762087.
   Web of Science ®Google Scholar
• Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. 2006. “Random Forests for Land Cover Classification.” Pattern Recognition Letters 27 (4): 294–300. doi:10.1016/j.patrec.2005.08.011.
   Web of Science ®Google Scholar
• Goetz, A. F. 2009. “Three Decades of Hyperspectral Remote Sensing of the Earth: A Personal View.” Remote Sensing of Environment 113: 5–16. doi:10.1016/j.rse.2007.12.014.
   Web of Science ®Google Scholar
• Goetz, A. F., G. Vane, J. E. Solomon, and B. N. Rock. 1985. “Imaging Spectrometry for Earth Remote Sensing.” Science 228 (4704): 1147–1153. doi:10.1126/science.228.4704.1147.
   PubMed Web of Science ®Google Scholar
• Govender, M., K. Chetty, and H. Bulcock. 2007. “A Review of Hyperspectral Remote Sensing and Its Application in Vegetation and Water Resource Studies.” Water Sa 33 (2): 145–151.
   Web of Science ®Google Scholar
• Green, R. O., M. L. Eastwood, C. M. Sarture, T. G. Chrien, M. Aronsson, B. J. Chippendale, J. A. Faust, B. E. Pavri, C. J. Chovit, and M. Solis. 1998. “Imaging Spectroscopy and the Airborne Visible/infrared Imaging Spectrometer (AVIRIS).” Remote Sensing of Environment 65 (3): 227–248. doi:10.1016/S0034-4257(98)00064-9.
   Web of Science ®Google Scholar
• Gu, Y., Y. Wang, Z. He, and H. Yue. 2015. “Hyperspectral Target Detection via Exploiting Spatial-spectral Joint Sparsity.” Neurocomputing 169: 5–12. doi:10.1016/j.neucom.2014.09.101.
   Web of Science ®Google Scholar
• Guo, Q., B. Zhang, Q. Ran, L. Gao, J. Li, and A. Plaza. 2014. “Weighted-RXD and Linear Filter-based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2351–2366. doi:10.1109/JSTARS.2014.2302446.
   Web of Science ®Google Scholar
• Hadeel, A., M. Jabbar, and X. Chen. 2011. “Remote Sensing and GIS Application in the Detection of Environmental Degradation Indicators.” Geo-spatial Information Science 14(1): 39-47. doi: 10.1007/s11806-011-0441-z.
   Google Scholar
• Halimi, A., Y. Altmann, N. Dobigeon, and J.-Y. Tourneret. 2011. “Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model.” IEEE Transactions on Geoscience and Remote Sensing 49 (11): 4153–4162. doi:10.1109/TGRS.2010.2098414.
   Web of Science ®Google Scholar
• Harsanyi, J. C., and C. Chang. 1994. “Hyperspectral Image Classification and Dimensionality Reduction: An Orthogonal Subspace Projection Approach.” IEEE Transactions on Geoscience and Remote Sensing 32 (4): 779–785. doi:10.1109/36.298007.
   Web of Science ®Google Scholar
• He, L., Q. Pan, W. Di, and Y. Li. 2008. “Anomaly Detection in Hyperspectral Imagery Based on Maximum Entropy and Nonparametric Estimation.” Pattern Recognition Letters 29 (9): 1392–1403. doi:10.1016/j.patrec.2008.02.012.
   Web of Science ®Google Scholar
• Hege, E. K., D. O’Connell, W. Johnson, S. Basty, and E. L. Dereniak. California, 2004. “Hyperspectral Imaging for Astronomy and Space Surveillance.” Proceedings of SPIE’s 48th Annual Meeting on Optical Science and Technology, pp. 380–391.
   Google Scholar
• Heylen, R., M. Parente, and P. Gader. 2014. “A Review of Nonlinear Hyperspectral Unmixing Methods.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 1844–1868. doi:10.1109/JSTARS.2014.2320576.
   Web of Science ®Google Scholar
• Heylen, R., and P. Scheunders. 2016. “A Multilinear Mixing Model for Nonlinear Spectral Unmixing.” IEEE Transactions on Geoscience and Remote Sensing 54 (1): 240–251. doi:10.1109/TGRS.2015.2453915.
   Web of Science ®Google Scholar
• Horn, B. K. P., and R. J. Woodham. 1979. “Destriping LANDSAT MSS Images by Histogram Modification.” Computer Graphics and Image Processing 10 (1): 69–83. doi:10.1016/0146-664X(79)90035-2.
   Google Scholar
• Hu, X., J. Liu, L. Sun, Z. Rong, Y. Li, Y. Zhang, Z. Zheng, et al. 2010. “Characterization of CRCS Dunhuang Test Site and Vicarious Calibration Utilization for Fengyun (FY) Series Sensors.” Canadian Journal of Remote Sensing 36 (5): 566–582. DOI:10.5589/m10-087.
   Web of Science ®Google Scholar
• Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira. 2002. “Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices.” Remote Sensing of Environment 83 (1–2): 195–213. doi:10.1016/S0034-4257(02)00096-2.
   Web of Science ®Google Scholar
• Hunt, E. R., Jr., W. D. Hively, S. J. Fujikawa, D. S. Linden, C. S. T. Daughtry, and G. W. McCarty. 2010. “Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring.” Remote Sensing 2: 290–305. doi:10.3390/rs2010290.
   Web of Science ®Google Scholar
• Huyan, N., X. Zhang, H. Zhou, and L. Jiao. 2019. “Hyperspectral Anomaly Detection via Background and Potential Anomaly Dictionaries Construction.” IEEE Transactions on Geoscience and Remote Sensing 57 (4): 2263–2276. doi:10.1109/TGRS.2018.2872590.
   Web of Science ®Google Scholar
• Iordache, M.-D., J. M. Bioucas-Dias, and A. Plaza. 2011. “Sparse Unmixing of Hyperspectral Data.” IEEE Transactions on Geoscience and Remote Sensing 49 (6): 2014–2039. doi:10.1109/TGRS.2010.2098413.
   Web of Science ®Google Scholar
• Iordache, M.-D., J. M. Bioucas-Dias, and A. Plaza. 2012. “Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing.” IEEE Transactions on Geoscience and Remote Sensing 50 (11): 4484–4502.
   Web of Science ®Google Scholar
• Jacobson, N. P., and M. R. Gupta. 2005. “Design Goals and Solutions for Display of Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 43 (11): 2684–2692. doi:10.1109/TGRS.2005.857623.
   Web of Science ®Google Scholar
• Jiang, Y., J. Wang, L. Zhang, G. Zhang, X. Li, and J. Wu. 2019. “Geometric Processing and Accuracy Verification of Zhuhai-1 Hyperspectral Satellites.” Remote Sensing 11 (9): 1–17. doi:10.3390/rs11090996.
   Web of Science ®Google Scholar
• Jiao, L., W. Sun, G. Yang, G. Ren, and Y. Liu. 2019. “A Hierarchical Classification Framework of Satellite Multispectral/hyperspectral Images for Mapping Coastal Wetlands.” Remote Sensing 11 (19): 1–19. doi:10.3390/rs11192238.
   Web of Science ®Google Scholar
• Keshava, N., and J. F. Mustard. 2002. “Spectral Unmixing.” IEEE Signal Processing Magazine 19 (1): 44–57. doi:10.1109/79.974727.
   Web of Science ®Google Scholar
• Khan, M. J., H. S. Khan, A. Yousaf, K. Khurshid, and A. Abbas. 2018. “Modern Trends in Hyperspectral Image Analysis: A Review.” IEEE Access 6: 14118–14129. doi:10.1109/ACCESS.2018.2812999.
   Web of Science ®Google Scholar
• Kraut, S., and L. L. Scharf. 1999. “The CFAR Adaptive Subspace Detector Is a Scale-invariant GLRT.” IEEE Transactions on Signal Processing 47 (9): 2538–2541. doi:10.1109/78.782198.
   Web of Science ®Google Scholar
• Kruse, F. A., J. W. Boardman, and J. F. Huntington. 2003. “Comparison of Airborne Hyperspectral Data and EO-1 Hyperion for Mineral Mapping.” IEEE Transactions on Geoscience and Remote Sensing 41 (6): 1388–1400. doi:10.1109/TGRS.2003.812908.
   Web of Science ®Google Scholar
• Lee, Z., and K. L. Carder. 2007. “Hyperspectral Remote Sensing.” Current Science 94 (9): 1115–1116.
   Google Scholar
• Lei, L., F. Jilu, H. Ling, Z. Jun, X. Xinliang, and L. Rui. 2018. “Mineral Mapping Using Spaceborne Tiangong-1 Hyperspectral Imagery and ASTER Data: A Case Study of Alteration Detection in Support of Regional Geological Survey at Jintanzi-Malianquan Area, Beishan, Gansu Province, China.” Geological Journal 53: 372–383. doi:10.1002/gj.3260.
   Web of Science ®Google Scholar
• Li, D., X. Zhao, and X. Li. 2016. “Remote Sensing of Human Beings – A Perspective from Nighttime Light.” Geo-spatial Information Science 19(1): 69–79. doi: 10.1080/10095020.2016.1159389.
   Google Scholar
• Li, J., L. Pu, M. Zhu, X. Dai, Y. Xu, X. Chen, L. Zhang, and R. Zhang. 2015. “Monitoring Soil Salt Content Using HJ-1A Hyperspectral Data: A Case Study of Coastal Areas in Rudong County, Eastern China.” Chinese Geographical Science 25 (2): 213–223. doi:10.1007/s11769-014-0693-2.
   Web of Science ®Google Scholar
• Li, Q., R. Zhong, and Y. Wang. 2019. “A Method for the Destriping of an Orbita Hyperspectral Image with Adaptive Moment Matching and Unidirectional Total Variation.” Remote Sensing 11 (18): 1–32.
   Web of Science ®Google Scholar
• Li, X., J. Wang, L. Zhang, T. Wu, and H. Jiang. Quebec, 2014. “A Combined Object-based Segmentation and Support Vector Machines Approach for Classification of Tiangong-01 Hyperspectral Urban Data.” IEEE International Geoscience and Remote Sensing Symposium, IEEE, pp. 1777–1780.
   Google Scholar
• Li, X., J. Xu, T. Cheng, H. Shi, X. Zhang, S. Ge, H. Wang, S. Zhu, J. Miao, and Q. Luo. 2019. “Monitoring Trace Gases over the Antarctic Using Atmospheric Infrared Ultraspectral Sounder Onboard GaoFen-5: Algorithm Description and First Retrieval Results of O3, H2O, and HCl.” Remote Sensing 11 (17): 1–18.
   Web of Science ®Google Scholar
• Li, X., T. Wu, K. Liu, Y. Li, and L. Zhang. 2016a. “Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-cover Classification.” Remote Sensing 8 (5): 1–17.
   Web of Science ®Google Scholar
• Li, X., Y. Zhang, J. Luo, X. Jin, Y. Xu, and W. Yang. 2016b. “Quantification Winter Wheat LAI with HJ-1CCD Image Features over Multiple Growing Seasons.” International Journal of Applied Earth Observation and Geoinformation 44: 104–112. doi:10.1016/j.jag.2015.08.004.
   Web of Science ®Google Scholar
• Liang, H. 2012. “Advances in Multispectral and Hyperspectral Imaging for Archaeology and Art Conservation.” Applied Physics A 106 (2): 309–323. doi:10.1007/s00339-011-6689-1.
   Web of Science ®Google Scholar
• Liang, L., L. Di, T. Huang, J. Wang, L. Lin, L. Wang, and M. Yang. 2018. “Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm.” Remote Sensing 10 (12): 1–16. doi:10.3390/rs10121940.
   Web of Science ®Google Scholar
• Liao, C., X. Zhang, and H. Bao. Shanghai, 2012. “Estimation of Fractional Vegetation Cover by Unmixing HJ-1 Satellite Hyperspectral Data.” 2012 Second International 2055 Workshop on Earth Observation and Remote Sensing Applications, pp. 278–281.
   Google Scholar
• Liu, L., J. Feng, B. Rivard, X. Xu, J. Zhou, L. Han, J. Yang, and G. Ren. 2018. “Mapping Alteration Using Imagery from the Tiangong-1 Hyperspectral Spaceborne System: Example for the Jintanzi Gold Province, China.” International Journal of Applied Earth Observation and Geoinformation 64: 275–286. doi:10.1016/j.jag.2017.03.013.
   Web of Science ®Google Scholar
• Liu, M., X. Liu, B. Zhang, and C. Ding. 2016. “Regional Heavy Metal Pollution in Crops by Integrating Physiological Function Variability with Spatio-temporal Stability Using Multi-temporal Thermal Remote Sensing.” International Journal of Applied Earth Observation Geoinformation 51: 91–102. doi:10.1016/j.jag.2016.05.003.
   Web of Science ®Google Scholar
• Liu, Y.-N., D.-X. Sun, X.-N. Hu, X. Ye, Y.-D. Li, S.-F. Liu, K.-Q. Cao, M.-Y. Chai, J. Zhang, and Y. Zhang. 2019a. “The Advanced Hyperspectral Imager: Aboard China’s GaoFen-5 Satellite.” IEEE Geoscience and Remote Sensing Magazine 7 (4): 23–32.
   Web of Science ®Google Scholar
• Liu, Y.-N., J. Zhang, Y. Zhang, W.-W. Sun, L.-L. Jiao, D.-X. Sun, X.-N. Hu, et al. 2019b. “The Advanced Hyperspectral Imager: Aboard China’s GaoFen-5 Satellite.” IEEE Geoscience and Remote Sensing Magazine 7 (4): 23–32. DOI:10.1109/MGRS.2019.2927687.
   Web of Science ®Google Scholar
• Liu, Z., Y. Lu, Y. Peng, L. Zhao, G. Wang, and Y. Hu. 2019c. “Estimation of Soil Heavy Metal Content Using Hyperspectral Data.” Remote Sensing 11 (12): 1–26.
   Web of Science ®Google Scholar
• Lu, G., and B. Fei. 2014. “Medical Hyperspectral Imaging: A Review.” Journal of Biomedical Optics 19 (1): 1–23. doi:10.1117/1.JBO.19.9.096013.
   Web of Science ®Google Scholar
• Lu, S., B. Wu, N. Yan, and H. Wang. 2011. “Water Body Mapping Method with HJ-1A/B Satellite Imagery.” International Journal of Applied Earth Observation and Geoinformation 13 (3): 428–434. doi:10.1016/j.jag.2010.09.006.
   Web of Science ®Google Scholar
• Lu, X., W. Zhang, and J. Huang. 2019. “Exploiting Embedding Manifold of Autoencoders for Hyperspectral Anomaly Detection.” IEEE Transactions on Geoscience and Remote Sensing 58 (3): 1527–1537. doi:10.1109/TGRS.2019.2944419.
   Web of Science ®Google Scholar
• Lv, Q., Y. Dou, X. Niu, J. Xu, and J. Xu. Guangzhou, 2015. “Classification of Tiangong-1 Hyperspectral Remote Sensing Image via Contextual Sparse Coding.” 2015 International 2100 Conference on Machine Learning and Cybernetics (ICMLC), vol. 1, pp. 128–133.
   Google Scholar
• Ma, W.-K., J. M. Bioucas-Dias, T.-H. Chan, N. Gillis, P. Gader, A. J. Plaza, A. Ambikapathi, et al. 2014. “A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing.” IEEE Signal Processing Magazine 31 (1): 67–81. DOI:10.1109/MSP.2013.2279731.
   Web of Science ®Google Scholar
• Manolakis, D., D. Marden, and G. A. Shaw. 2003. “Hyperspectral Image Processing for Automatic Target Detection Applications.” Lincoln Laboratory Journal 14 (1): 79–116.
   Google Scholar
• Manolakis, D. G. 2005. “Taxonomy of Detection Algorithms for Hyperspectral Imaging Applications.” IEEE Signal Processing Magazine 19 (1): 29–43. doi:10.1109/79.974724.
   Google Scholar
• Marzi, D., A. Marinoni, and P. Gamba. Yokohama, 2019. “Mapping Mineral Abundances on the Moon Surface Using Chang’E-1 IIM Data.” IEEE International Geoscience and Remote Sensing Symposium, pp. 4917-4920.
   Google Scholar
• Matteoli, S., M. Diani, and G. Corsini. 2010. “Improved Estimation of Local Background Covariance Matrix for Anomaly Detection in Hyperspectral Images.” Optical Engineering 49 (4): 1–16. doi:10.1117/1.3386069.
   Web of Science ®Google Scholar
• Matteoli, S., T. Veracini, M. Diani, and G. Corsini. 2013. “Models and Methods for Automated Background Density Estimation in Hyperspectral Anomaly Detection.” IEEE Transactions on Geoscience and Remote Sensing 51 (5): 2837–2852. doi:10.1109/TGRS.2012.2214392.
   Web of Science ®Google Scholar
• Mei, S., X. Yuan, J. Ji, S. Wan, J. Hou, and Q. Du. Yokohama, 2017a. “Hyperspectral Image Super-resolution via Convolutional 2130 Neural Network.” 2017 IEEE International Conference on Image Processing (ICIP), pp. 4297-4301.
   Google Scholar
• Mei, S., X. Yuan, J. Ji, Y. Zhang, S. Wan, and Q. Du. 2017b. “Hyperspectral Image Spatial Super-resolution via 3D Full Convolutional Neural Network.” Remote Sensing 9 (11): 1–22.
   Web of Science ®Google Scholar
• Melgani, F., and L. Bruzzone. 2004. “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42 (8): 1778–1790. doi:10.1109/TGRS.2004.831865.
   Web of Science ®Google Scholar
• Meng, X., and J. Cheng. 2020. “Estimating Land and Sea Surface Temperature from Cross-calibrated Chinese Gaofen-5 Thermal Infrared Data Using Split-window Algorithm.” IEEE Geoscience and Remote Sensing Letters 17 (3): 509–513. doi:10.1109/LGRS.2019.2921863.
   Web of Science ®Google Scholar
• Mi, W., Q. Kailing, C. Yufeng, Z. Ying, and G. Beibei. 2019. “Geometric Calibration for GF-5 AHSI Camera.” Journal of Remote Sensing, 24 (4): 345–351.
   PubMedGoogle Scholar
• Mianji, F. A., and Y. Zhang. 2011. “Robust Hyperspectral Classification Using Relevance Vector Machine.” IEEE Transactions on Geoscience and Remote Sensing 49 (6): 2100–2112. doi:10.1109/TGRS.2010.2103381.
   Web of Science ®Google Scholar
• Miao, L., and H. Qi. 2007. “Endmember Extraction from Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization.” IEEE Transactions on Geoscience and Remote Sensing 45 (3): 765–777. doi:10.1109/TGRS.2006.888466.
   Web of Science ®Google Scholar
• Michael, E. W. Colorado, 1999. “N-FINDR: An Algorithm for Fast Autonomous Spectral End-member Determination in 2160 Hyperspectral Data.” SPIE’s International Symposium on Optical Science, Engineering, and Instrumentation on Imaging Spectrometry V, vol. 3753, pp. 266–275.
   Google Scholar
• Nascimento, J. M. P., and J. M. B. Dias. 2005. “Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data.” IEEE Transactions on Geoscience and Remote Sensing 43 (4): 898–910. doi:10.1109/TGRS.2005.844293.
   Web of Science ®Google Scholar
• Nasrabadi, N. 2014. “Hyperspectral Target Detection: An Overview of Current and Future Challenges.” IEEE Signal Processing Magazine 31 (1): 34–44. doi:10.1109/MSP.2013.2278992.
   Web of Science ®Google Scholar
• Nasrabadi, N. M. Florida, 2008. “Regularization for Spectral Matched Filter and RX Anomaly Detector.” SPIE Defense and Security Symposium on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, vol. 6966.
   Google Scholar
• Pal, M. 2009. “Extreme-learning-machine-based Land Cover Classification.” International Journal of Remote Sensing 30 (14): 3835–3841. doi:10.1080/01431160902788636.
   Web of Science ®Google Scholar
• Pan, J.-J., and C.-I. Chang. 1992. “Destriping of Landsat MSS Images by Filtering Techniques.” Photogrammetric Engineering and Remote Sensing 58: 1417.
   Web of Science ®Google Scholar
• Pan, Z., J. Huang, Q. Zhou, L. Wang, Y. Cheng, H. Zhang, G. A. Blackburn, J. Yan, and J. Liu. 2015. “Mapping Crop Phenology Using NDVI Time-series Derived from HJ-1 A/B Data.” International Journal of Applied Earth Observation and Geoinformation 34: 188–197. doi:10.1016/j.jag.2014.08.011.
   Web of Science ®Google Scholar
• Pang, B., L. Ma, Y. Liu, N. Wang, Y. Zhao, Q. Han, F. Meng, et al. 2019. “Ground-based Automatic Radiometric Calibration of Land Observation Satellite Optical Sensors and Cross Validation Analysis.” Remote Sensing Technology and Application 34 (1): 146–154.
   Google Scholar
• Pang, W., X. Zheng, J. Lu, and J. Li. 2015. “Reflectance Characteristics of Dunhuang Radiometric Calibration Test Site.” Journal of Atmospheric and Environmental Optics 10 (6): 472–481.
   Google Scholar
• Penuelas, J., J. A. Gamon, A. L. Fredeen, J. Merino, and C. B. Field. 1994. “Reflectance Indexes Associated with Physiological-changes in Nitrogen-limited and Water-limited Sunflower Leaves.” Remote Sensing of Environment 48 (2): 135–146. doi:10.1016/0034-4257(94)90136-8.
   Web of Science ®Google Scholar
• Qi, J., A. Chehbouni, A. R. Huete, Y. H. Kerr, and S. Sorooshian. 1994. “A Modified Soil Adjusted Vegetation Index.” Remote Sensing of Environment 48 (2): 119–126. doi:10.1016/0034-4257(94)90134-1.
   Web of Science ®Google Scholar
• Qi, W., X. Zhang, N. Wang, M. Zhang, and Y. Cen. 2019. “A Spectral-Spatial Cascaded 3D Convolutional Neural Network with A Convolutional Long Short-Term Memory Network for Hyperspectral Image Classification.” Remote Sensing 11 (20): 1–20. doi:10.3390/rs11202363.
   Web of Science ®Google Scholar
• Qian, S.-E., and G. Chen. 2012. “Enhancing Spatial Resolution of Hyperspectral Imagery Using Sensor’s Intrinsic Keystone Distortion.” IEEE Transactions on Geoscience and Remote Sensing 50 (12): 5033–5048. doi:10.1109/TGRS.2012.2195666.
   Web of Science ®Google Scholar
• Qian, Y., S. Jia, J. Zhou, and A. Robles-Kelly. 2011. “Hyperspectral Unmixing via L1/2 Sparsity-constrained Nonnegative Matrix Factorization.” IEEE Transactions on Geoscience and Remote Sensing 49 (11): 4282–4297. doi:10.1109/TGRS.2011.2144605.
   Web of Science ®Google Scholar
• Reed, I. S., and X. Yu. 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:10.1109/29.60107.
   Google Scholar
• Ren, H., X. Ye, R. Liu, J. Dong, and Q. Qin. 2018. “Improving Land Surface Temperature and Emissivity Retrieval from the Chinese Gaofen-5 Satellite Using a Hybrid Algorithm.” IEEE Transactions on Geoscience and Remote Sensing 56 (2): 1080–1090. doi:10.1109/TGRS.2017.2758804.
   Web of Science ®Google Scholar
• Ren, K., W. Sun, X. Meng, G. Yang, and Q. Du. 2020. “Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?” Remote Sensing 12 (5): 1–26. doi:10.3390/rs12050882.
   Web of Science ®Google Scholar
• Rickard, L. J., R. W. Basedow, E. F. Zalewski, P. R. Silverglate, and M. Landers. Florida, 1993. “HYDICE: An airborne system for hyperspectral imaging,” in Imaging Spectrometry of the Terrestrial Environment, vol. 1937: International Society for Optics and Photonics, pp. 173-179.
   Google Scholar
• Rudin, L. I., S. Osher, and E. Fatemi. 1992. “Nonlinear Total Variation Based Noise Removal Algorithms.” Physica D. Nonlinear Phenomena 60 (1–4): 259–268. doi:10.1016/0167-2789(92)90242-F.
   Web of Science ®Google Scholar
• Sakla, W., A. Chan, J. Ji, and A. Sakla. 2011. “An SVDD-based Algorithm for Target Detection in Hyperspectral Imagery.” IEEE Geoscience and Remote Sensing Letters 8 (2): 384–388. doi:10.1109/LGRS.2010.2078795.
   Web of Science ®Google Scholar
• Scharf, L. L., and B. Friedlander. 1993. “Matched Subspace Detectors.” IEEE Transactions on Signal Processing 42 (8): 2146–2157. doi:10.1109/78.301849.
   Web of Science ®Google Scholar
• Shi, F., X. Gao, L. Yang, L. He, and W. Jia. 2017. “Research on Typical Crop Classification Based on HJ-1A Hyperspectral Data in the Huangshui River Basin.” Remote Sensing Technology and Application 32 (2): 206–217.
   Google Scholar
• Shu, X., J. Wang, M. Sheng, and D. Kuang. 2000. “Remote Sensing of Water Quality Monitoring Using an Airborne Imaging Spectrometer.” Journal of Infrared and Millimeter Waves 19 (4): 273–276.
   Web of Science ®Google Scholar
• Song, X., J. Wang, L. Liu, X. Xue, and C. Zhao. Alaska, 2004. “Use of 2265 Airborne Hyperspectral Image Data to Assess Winter Wheat Yield.” IEEE International Geoscience and Remote Sensing Symposium, vol. 6, pp. 4080–4083.
   Google Scholar
• Stein, D. W. J., S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum, and A. D. Stocker. 2002. “Anomaly Detection from Hyperspectral Imagery.” IEEE Signal Processing Magazine 19 (1): 58–69. doi:10.1109/79.974730.
   Web of Science ®Google Scholar
• Sun, W., C. Liu, J. Li, Y. M. Lai, and W. Li. 2014. “Low-rank and Sparse Matrix Decomposition-based Anomaly Detection for Hyperspectral Imagery.” Journal of Applied Remote Sensing 8 (1): 1–18.
   Google Scholar
• Tang, B.-H. 2018. “Nonlinear Split-window Algorithms for Estimating Land and Sea Surface Temperatures from Simulated Chinese Gaofen-5 Satellite Data.” IEEE Transactions on Geoscience and Remote Sensing 56 (11): 6280–6289. doi:10.1109/TGRS.2018.2833859.
   Web of Science ®Google Scholar
• Tao, C., H. Pan, Y. Li, and Z. Zou. 2015. “Unsupervised Spectral-Spatial Feature Learning with Stacked Sparse Autoencoder for Hyperspectral Imagery Classification.” IEEE Geoscience and Remote Sensing Letters 12 (12): 2438–2442. doi:10.1109/LGRS.2015.2482520.
   Web of Science ®Google Scholar
• Tarantola, A., and B. Valette. 1982. “Generalized Nonlinear Inverse Problems Solved Using the Least Squares Criterion.” Reviews of Geophysics 20 (2): 219–232. doi:10.1029/RG020i002p00219.
   Web of Science ®Google Scholar
• Tian, Y. C., X. Yao, J. Yang, W. X. Cao, D. B. Hannaway, and Y. Zhu. 2011. “Assessing Newly Developed and Published Vegetation Indices for Estimating Rice Leaf Nitrogen Concentration with Ground- and Space-based Hyperspectral Reflectance.” Field Crops Research 120 (2): 299–310. doi:10.1016/j.fcr.2010.11.002.
   Web of Science ®Google Scholar
• Torres, J., and S. O. Infante. 2001. “Wavelet Analysis for the Elimination of Striping Noise in Satellite Images.” Optical Engineering 40: 1309–1314. doi:10.1117/1.1383996.
   Web of Science ®Google Scholar
• Trebesch, C., and R. W. Kolb. 2009. “Is There a Best Hyperspectral Detection Algorithm?” Proceedings of SPIE - the International Society for Optical Engineering 7334: 1–16.
   Google Scholar
• Ungar, S. G., J. S. Pearlman, J. A. Mendenhall, and D. Reuter. 2003. “Overview of the Earth Observing One (EO-1) Mission.” IEEE Transactions on Geoscience and Remote Sensing 41 (6): 1149–1159. doi:10.1109/TGRS.2003.815999.
   Web of Science ®Google Scholar
• Vane, G., A. F. Goetz, and J. B. Wellman. 1984. “Airborne Imaging Spectrometer: A New Tool for Remote Sensing.” IEEE Transactions on Geoscience and Remote Sensing GE-22 (6): 546–549. doi:10.1109/TGRS.1984.6499168.
   Web of Science ®Google Scholar
• Wan, L., Y. Lin, H. Zhang, F. Wang, M. Liu, and H. Lin. 2020. “GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong.” Remote Sensing 12 (4): 1–16. doi:10.3390/rs12040656.
   Web of Science ®Google Scholar
• Wang, H., J. Zhang, J. Wu, and Z. Yan. 2017a. “Research on Mangrove Recognition Based on Hyperspectral Unmixing.” IEEE International Conference on Unmanned Systems (ICUS), Beijing, pp. 298–300.
   Google Scholar
• Wang, J., J. Huang, K. Zhang, X. Li, B. She, C. Wei, J. Gao, and X. Song. 2015a. “Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images.” Remote Sensing 7 (4): 3467–3488. doi:10.3390/rs70403467.
   Web of Science ®Google Scholar
• Wang, M., K.-M. Yang, and G.-P. Wang. 2020. “Relative Radiometric Calibration of Yaw Data without Calibration Field of Hyperspectral Images Based on Harmonic Analysis.” International Journal of Remote Sensing 41 (14): 5429–5442. doi:10.1080/01431161.2020.1731933.
   Web of Science ®Google Scholar
• Wang, M., S. Zhou, M. He, and X.-Y. Chen. 2015b. “Characterization and Calibration Method of Satellite Sensor Radiometric Calibration Site.” Geomatics Spatial Information Technology 38 (7): 24–27.
   Google Scholar
• Wang, H., J. Zhang, J. Wu, and Z. Yan. Beijing, 2017a. “Research on Mangrove Recognition Based on Hyperspectral Unmixing.” IEEE International Conference on Unmanned Systems (ICUS), pp. 298-300.
   Google Scholar
• Wang, X., Y. Zhong, L. Zhang, and Y. Xu. 2017b. “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.2017.2724944.
   Web of Science ®Google Scholar
• Wei, Q., J. Bioucas-Dias, N. Dobigeon, and J. Y. Tourneret. 2015. “Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation.” IEEE Transactions on Geoscience and Remote Sensing 53 (7): 3658–3668. doi:10.1109/TGRS.2014.2381272.
   Web of Science ®Google Scholar
• Wei, Q., M. Chen, J. Tourneret, and S. Godsill. 2017. “Unsupervised Nonlinear Spectral Unmixing Based on a Multilinear Mixing Model.” IEEE Transactions on Geoscience and Remote Sensing 55 (8): 4534–4544. doi:10.1109/TGRS.2017.2693366.
   Web of Science ®Google Scholar
• Wright, J., A. Ganesh, S. Rao, Y. Peng, and Y. Ma. 2009. “Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization,” Advances in neural information processing systems, vol. 22, pp. 2080–2088.
   Google Scholar
• Wu, X., X. Zhang, N. Wang, and Y. Cen. 2019. “Joint Sparse and Low-rank Multi-task Learning with Extended Multi-attribute Profile for Hyperspectral Target Detection.” Remote Sensing 11 (2): 1–17.
   Web of Science ®Google Scholar
• Xi, Y., C. Ren, Z. Wang, S. Wei, J. Bai, B. Zhang, H. Xiang, and L. Chen. 2019. “Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China.” Forests 10 (9): 1–17. doi:10.3390/f10090818.
   Web of Science ®Google Scholar
• Xia, J., N. Yokoya, and A. Iwasaki. 2016. “Hyperspectral Image Classification with Canonical Correlation Forests.” IEEE Transactions on Geoscience and Remote Sensing 55 (1): 421–431. doi:10.1109/TGRS.2016.2607755.
   Web of Science ®Google Scholar
• Xia, J., P. Du, X. He, and J. Chanussot. 2014. “Hyperspectral Remote Sensing Image Classification Based on Rotation Forest.” IEEE Geoscience and Remote Sensing Letters 11 (1): 239–243. doi:10.1109/LGRS.2013.2254108.
   Web of Science ®Google Scholar
• Xiangchao, M., S. Weiwei, R. Kai, Y. Gang, S. Feng, and F. Randi. 2020. “Spatial-spectral Fusion of GF-5/GF-1 Remote Sensing Images Based on Multiresolution Analysis.” Journal of Remote Sensing 24 (4): 379–387.
   Google Scholar
• Xiao, L., P. Zhu, G. Fang, Z. Xiao, Y. Zou, J. Zhao, N. Zhao, et al. 2015. “A Young Multilayered Terrane of the Northern Mare Imbrium Revealed by Chang’E-3 Mission.” Science 347 (6227): 1226–1229. DOI:10.1126/science.1259866.
   PubMed Web of Science ®Google Scholar
• Xie, W., J. Lei, B. Liu, Y. Li, and X. Jia. 2019. “Spectral Constraint Adversarial Autoencoders Approach to Feature Representation in Hyperspectral Anomaly Detection.” Neural Networks 119: 222–234.
   PubMed Web of Science ®Google Scholar
• Xu, W., M. J. Wooster, and C. S. B. Grimmond. 2008. “Modelling of Urban Sensible Heat Flux at Multiple Spatial Scales: A Demonstration Using Airborne Hyperspectral Imagery of Shanghai and A Temperature–emissivity Separation Approach.” Remote Sensing of Environment 112 (9): 3493–3510. doi:10.1016/j.rse.2008.04.009.
   Web of Science ®Google Scholar
• Xu, Y., B. Du, and L. Zhang. 2020. “Beyond the Patchwise Classification: Spectral-spatial Fully Convolutional Networks for Hyperspectral Image Classification.” IEEE Transactions on Big Data 6 (3): 492–506. doi:10.1109/TBDATA.2019.2923243.
   Web of Science ®Google Scholar
• Yang, B., and B. Wang. 2018. “Band-Wise Nonlinear Unmixing for Hyperspectral Imagery Using an Extended Multilinear Mixing Model.” IEEE Transactions on Geoscience and Remote Sensing 56 (11): 6747–6762. doi:10.1109/TGRS.2018.2842707.
   Web of Science ®Google Scholar
• Yang, D., Y. Liu, Z. Cai, X. Chen, L. Yao, and D. Lu. 2018. “First Global Carbon Dioxide Maps Produced from TanSat Measurements.” Advances in Atmospheric Sciences 35 (6): 621–623. doi:10.1007/s00376-018-7312-6.
   Web of Science ®Google Scholar
• Yang, K. M., Y. H. Chen, D. Z. Guo, and J. B. Jiang. 2008. “Spectral Information Detection and Extraction of Wheat Stripe Rust Based on Hyperspectral Image.” Acta Photonica Sinica 37 (1): 145–151.
   Google Scholar
• Yang, X., Z. Wu, J. Li, A. Plaza, and Z. Wei. 2016. “Anomaly Detection in Hyperspectral Images Based on Low-rank and Sparse Representation.” IEEE Transactions on Geoscience and Remote Sensing 54 (4): 1990–2000. doi:10.1109/TGRS.2015.2493201.
   Web of Science ®Google Scholar
• Yang, Z., Y. Shao, K. Li, Q. Liu, L. Liu, and B. Brisco. 2017. “An Improved Scheme for Rice Phenology Estimation Based on Time-series Multispectral HJ-1A/B and Polarimetric RADARSAT-2 Data.” Remote Sensing of Environment 195: 184–201. doi:10.1016/j.rse.2017.04.016.
   Web of Science ®Google Scholar
• Yao, D., L. Zhuang, L. Gao, B. Zhang, and J. M. Bioucas-Dias. Texas, 2017. “Hyperspectral Image Inpainting Based on Low-rank Representation: A Case Study on Tiangong-1 1810 Data.” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3409–3412.
   Google Scholar
• Ye, X., H. Ren, R. Liu, Q. Qin, Y. Liu, and J. Dong. 2017. “Land Surface Temperature Estimate from Chinese Gaofen-5 Satellite Data Using Split-window Algorithm.” IEEE Transactions on Geoscience and Remote Sensing 55 (10): 5877–5888. doi:10.1109/TGRS.2017.2716401.
   Web of Science ®Google Scholar
• Ying, Q., W. Wei, G. Rui, B. Ayhan, C. Kwan, S. Vance, and H. Qi. 2018. “Hyperspectral Anomaly Detection through Spectral Unmixing and Dictionary-Based Low-Rank Decomposition.” IEEE Transactions on Geoscience and Remote Sensing PP (99): 1–15.
   Google Scholar
• Yu, H., B. Kong, G. Wang, R. Du, and G. Qie. 2018. “Prediction of Soil Properties Using a Hyperspectral Remote Sensing Method.” Archives of Agronomy and Soil Science 64 (4): 546–559. doi:10.1080/03650340.2017.1359416.
   Web of Science ®Google Scholar
• Zhang, H., B. Zhang, Z. Chen, and Z. Huang. 2018. “Vicarious Radiometric Calibration of the Hyperspectral Imaging Microsatellites SPARK-01 And-02 over Dunhuang, China.” Remote Sensing 10 (1): 1–36.
   Web of Science ®Google Scholar
• Zhang, H., L. Zhang, and H. Shen. 2012. “A Super-resolution Reconstruction Algorithm for Hyperspectral Images.” Signal Processing 92 (9): 2082–2096. doi:10.1016/j.sigpro.2012.01.020.
   Web of Science ®Google Scholar
• Zhang, L. and B. Du. 2012. “Recent Advances in Hyperspectral Image Processing.” Geo-spatial Information Science 15(3): 143–156. doi: 10.1080/10095020.2012.719684.
   Google Scholar
• Zhang, Q., Q. Yuan, J. Li, X. Liu, H. Shen, and L. Zhang. 2019. “Hybrid Noise Removal in Hyperspectral Imagery with a Spatial–spectral Gradient Network.” IEEE Transactions on Geoscience and Remote Sensing 57 (10): 7317–7329. doi:10.1109/TGRS.2019.2912909.
   Web of Science ®Google Scholar
• Zhang, X., and J. Li. 2019. “Application of Zhuhai-1 Hyperspectral Satellite in Fine Classification of Crops in Gaomi City.” Satellite Application 8: 29–33.
   Google Scholar
• Zhang, Y., B. Du, Y. Zhang, and L. Zhang. 2017. “Spatially Adaptive Sparse Representation for Target Detection in Hyperspectral Images.” IEEE Geoscience and Remote Sensing Letters PP (99): 1–5.
   Google Scholar
• Zhang, Y., D. Bo, L. Zhang, and S. Wang. 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): 1–14.
   Web of Science ®Google Scholar
• Zhang, Y., S. De Backer, and P. Scheunders. 2009. “Noise-resistant Wavelet-based Bayesian Fusion of Multispectral and Hyperspectral Images.” IEEE Transactions on Geoscience and Remote Sensing 47 (11): 3834–3843. doi:10.1109/TGRS.2009.2017737.
   Web of Science ®Google Scholar
• Zhao, C., and L. Zhang. 2018. “Spectral-spatial Stacked Autoencoders Based on Low-rank and Sparse Matrix Decomposition for Hyperspectral Anomaly Detection.” Infrared Physics & Technology 92: 166–176. doi:10.1016/j.infrared.2018.06.001.
   Web of Science ®Google Scholar
• Zhao, C., X. Li, and H. Zhu. 2017. “Hyperspectral Anomaly Detection Based on Stacked Denoising Autoencoders.” Journal of Applied Remote Sensing 11 (4): 1–19. doi:10.1117/1.JRS.11.042605.
   Web of Science ®Google Scholar
• Zhao, C., X. Li, J. Ren, and S. Marshall. 2013. “Improved Sparse Representation Using Adaptive Spatial Support for Effective Target Detection in Hyperspectral Imagery.” International Journal for Remote Sensing 34 (24): 8669–8684. doi:10.1080/01431161.2013.845924.
   Web of Science ®Google Scholar
• Zhao, Y., Y. Zeng, Z. Zheng, W. Dong, D. Zhao, B. Wu, and Q. Zhao. 2018. “Forest Species Diversity Mapping Using Airborne LiDAR and Hyperspectral Data in a Subtropical Forest in China.” Remote Sensing of Environment 213: 104–114. doi:10.1016/j.rse.2018.05.014.
   Web of Science ®Google Scholar
• Zheng, Z., Y. Zhong, A. Ma, and L. Zhang. 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.2020.2967821.
   Web of Science ®Google Scholar
• Zhong, Y., R. Feng, and L. Zhang. 2013. “Non-local Sparse Unmixing for Hyperspectral Remote Sensing Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 1889–1909. doi:10.1109/JSTARS.2013.2280063.
   Web of Science ®Google Scholar
• Zhong, Y., W. Li, X. Wang, S. Jin, and L. Zhang. 2020. “Satellite-ground Integrated Destriping Network: A New Perspective for EO-1 Hyperion and Chinese Hyperspectral Satellite Datasets.” Remote Sensing of Environment 237: 111416. doi:10.1016/j.rse.2019.111416.
   Web of Science ®Google Scholar
• Zhou, L., D. A. Roberts, W. Ma, H. Zhang, and L. Tang. 2014. “Estimation of Higher Chlorophylla Concentrations Using Field Spectral Measurement and HJ-1A Hyperspectral Satellite Data in Dianshan Lake, China.” ISPRS Journal of Photogrammetry and Remote Sensing 88: 41–47.
   Web of Science ®Google Scholar
• Zhou, X., W. Huang, W. Kong, H. Ye, and U. Luo. 2016. “Remote Estimation of Canopy Nitrogen Content in Winter Wheat Using Airborne Hyperspectral Reflectance Measurements.” Advances in Space Research 58: 1627–1637. doi:10.1016/j.asr.2016.06.034.
   Web of Science ®Google Scholar
• Zhu, H., G. Lianru, Z. Bing, S. Xu, and L. Qingting. 2019. “Nonlinear Hyperspectral Unmixing Algorithm Based on Deep Autoencoder Networks.” Journal of Remote Sensing 24 (4): 388-400.
   Google Scholar