{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T19:57:10Z","timestamp":1760731030826,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,21]],"date-time":"2018-02-21T00:00:00Z","timestamp":1519171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Many methods based on radiative-transfer models and empirical approaches with prior knowledge have been developed for the retrieval of hyperspectral surface reflectance. In this paper, we propose a novel approach for atmospheric correction of hyperspectral images based on machine learning. A support vector machine (SVM) is used for learning to predict the surface reflectance from the preprocessed at-sensor radiance image. The preprocessed spectra of each pixel are considered as the spectral feature and hypercolumn based on convolutional neural networks (CNNs) is utilized for spatial feature extraction. After training, the surface reflectance of images from totally different spatial positions and atmospheric conditions can be quickly predicted with the at-sensor radiance image and the models trained before, and no additional metadata is required. On an AVIRIS hyperspectral data set, the performances of our method, based on spectral and spatial features, respectively, are compared. Furthermore, our method outperforms QUAC, and the retrieved spectra have good agreement with FLAASH and AVIRIS reflectance products.<\/jats:p>","DOI":"10.3390\/rs10020323","type":"journal-article","created":{"date-parts":[[2018,2,21]],"date-time":"2018-02-21T12:40:47Z","timestamp":1519216847000},"page":"323","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Retrieval of Hyperspectral Surface Reflectance Based on Machine Learning"],"prefix":"10.3390","volume":"10","author":[{"given":"Sijie","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100039, China"},{"name":"Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Beijing 100190, China"}]},{"given":"Bin","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100039, China"},{"name":"Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Beijing 100190, China"}]},{"given":"Yirong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100039, China"},{"name":"Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0034-4257(98)00064-9","article-title":"Imaging spectroscopy and the airborne visible\/infrared imaging spectrometer (AVIRIS)","volume":"65","author":"Green","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/0034-4257(93)90014-O","article-title":"Derivation of scaled surface reflectances from AVIRIS data","volume":"44","author":"Gao","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1109\/TGRS.2003.813125","article-title":"The high accuracy atmospheric correction for hyperspectral data (HATCH) model","volume":"41","author":"Qu","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","unstructured":"Kruse, F. (April, January 31). Comparison of ATREM, ACORN, and FLAASH atmospheric corrections using low-altitude AVIRIS data of Boulder, CO. Proceedings of the 13th JPL Airborne Geoscience Workshop, Jet Propulsion Lab, Pasadena, CA, USA."},{"key":"ref_5","unstructured":"Cooley, T., Anderson, G.P., Felde, G.W., Hoke, M.L., Ratkowski, A.J., Chetwynd, J.H., Gardner, J.A., Adler-Golden, S.M., Matthew, M.W., and Berk, A. (2002, January 24\u201328). FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1117\/12.366315","article-title":"Atmospheric correction for shortwave spectral imagery based on MODTRAN4","volume":"3753","author":"Adlergolden","year":"1999","journal-title":"Proc SPIE"},{"key":"ref_7","first-page":"83","article-title":"Mineral mapping at Cuprite, Nevada with a 63-channel imaging spectrometer","volume":"56","author":"Kruse","year":"1990","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","unstructured":"Bernstein, L.S., Adler-Golden, S.M., Sundberg, R.L., Levine, R.Y., Perkins, T.C., Berk, A., Ratkowski, A.J., Felde, G., and Hoke, M.L. (2005, January 29). A new method for atmospheric correction and aerosol optical property retrieval for VIS-SWIR multi- and hyperspectral imaging sensors: QUAC (QUick atmospheric correction). Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1117\/1.OE.51.11.111719","article-title":"Quick atmospheric correction code: Algorithm description and recent upgrades","volume":"51","author":"Bernstein","year":"2012","journal-title":"Opt. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1109\/TGRS.2003.820314","article-title":"A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data","volume":"42","author":"Plaza","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Kavukcuoglu, K., and Farabet, C. (June, January 30). Convolutional networks and applications in vision. Proceedings of the 2010 IEEE International Symposium on Circuits and Systems, Paris, France.","DOI":"10.1109\/ISCAS.2010.5537907"},{"key":"ref_13","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2015). Very deep convolutional networks for large-scale image recognition, arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TGRS.2015.2476502","article-title":"Object Classification and Recognition From Mobile Laser Scanning Point Clouds in a Road Environment","volume":"54","author":"Jaakkola","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/TGRS.2016.2551720","article-title":"Target Classification Using the Deep Convolutional Networks for SAR Images","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/TGRS.2013.2258676","article-title":"SVM Active Learning Approach for Image Classification Using Spatial Information","volume":"52","author":"Pasolli","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.1109\/TGRS.2014.2357078","article-title":"Saliency-Guided Unsupervised Feature Learning for Scene Classification","volume":"53","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/LGRS.2009.2023605","article-title":"Machine learning and bias correction of MODIS aerosol optical depth","volume":"6","author":"Lary","year":"2009","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"16398","DOI":"10.3390\/rs71215841","article-title":"Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data","volume":"7","author":"Ali","year":"2015","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Girshick, R., and Malik, J. (2015, January 7\u201312). Hypercolumns for object segmentation and fine-grained localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Larsson, G., Maire, M., and Shakhnarovich, G. (2016, January 11\u201314). Learning representations for automatic colorization. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_35"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Penatti, O.A., Nogueira, K., and dos Santos, J.A. (2015, January 7\u201312). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref_27","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1162\/089976600300015565","article-title":"New Support Vector Algorithms","volume":"12","author":"Smola","year":"2000","journal-title":"Neural Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/36.20292","article-title":"MODIS: Advanced facility instrument for studies of the Earth as a system","volume":"27","author":"Salomonson","year":"1989","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/323\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:55:47Z","timestamp":1760194547000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/323"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,21]]},"references-count":30,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["rs10020323"],"URL":"https:\/\/doi.org\/10.3390\/rs10020323","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,2,21]]}}}