• Journal of Infrared and Millimeter Waves
  • Vol. 37, Issue 4, 477 (2018)
GAO Jun*, WANG Kai, TIAN Xiao-Yu, and CHEN Jian
Author Affiliations
  • [in Chinese]
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    DOI: 10.11972/j.issn.1001-9014.2018.04.016 Cite this Article
    GAO Jun, WANG Kai, TIAN Xiao-Yu, CHEN Jian. A BP-NN based cloud detection method for FY-4 remote sensing images[J]. Journal of Infrared and Millimeter Waves, 2018, 37(4): 477 Copy Citation Text show less
    References

    [1] Kriebel K T, Gesell G, Kstner M, et alet al. The cloud analysis tool APOLLO: Improvements and validations[J]. International Journal of Remote SensingInternational Journal of Remote Sensing, 2003, 24(12): 2389-2408.

    [2] Krner O, Di Girolamo L. On automatic cloud detection over ocean[J]. International Journal of Remote SensingInternational Journal of Remote Sensing, 2001, 22(15): 3047-3052.

    [3] Rossow W B, Schiffer R A. ISCCP cloud data products[J]. Bulletin of the American Meteorological SocietyBulletin of the American Meteorological Society, 1991, 72(1): 2-20.

    [4] Team M C M, Ackerman S, Strabala K, et alet al. Discriminating clear-sky from cloud with modis algorithm theoretical basis document (mod35)[J]. ATBD Ref. ATBD-MOD-06, version ATBD Ref. ATBD-MOD-06, version, 1997, 4: 115p.

    [5] Mouri K, Izumi T, Suzue H, et alet al. Algorithm theoretical basis document of cloud type/phase product[J]. Meteorological Satellite Center Technical NoteMeteorological Satellite Center Technical Note, 2016, 61:19-31.

    [6] Bankert R L. Cloud classification of AVHRR imagery in maritime regions using a probabilistic neural network[J]. Journal of Applied MeteorologyJournal of Applied Meteorology, 1994, 33(8): 909-918.

    [7] Azimi-Sadjadi M R, Shaikh M A, Tian B, et alet al. Neural network-based cloud detection/classification using textural and spectral features[J]. Geoscience and Remote Sensing Symposium, 1996. IGARSS ’96. “Remote Sensing for a Sustainable Future.”, InternationalGeoscience and Remote Sensing Symposium, 1996. IGARSS ’96. “Remote Sensing for a Sustainable Future.”, International. 1996, 2: 1105-1107.

    [8] Tian B, Shaikh M A, Azimi-Sadjadi M R, et alet al. A study of cloud classification with neural networks using spectral and textural features[J]. IEEE Transactions on Neural Networks IEEE Transactions on Neural Networks, 1999, 10(1): 138-151.

    [9] Jang J, Viau A A, Anctil F, et alet al. Neural network application for cloud detection in SPOT VEGETATION images[J]. International Journal of Remote SensingInternational Journal of Remote Sensing, 2006, 27(4): 719-736.

    [10] Zhang W D, He M X, Mak M W. Cloud detection using probabilistic neural networks[J]. Geoscience and Remote Sensing Symposium, 2001. IGARSS’01. IEEE 2001 International. IEEEGeoscience and Remote Sensing Symposium, 2001. IGARSS’01. IEEE 2001 International. IEEE, 2001, 5: 2373-2375.

    [15] Gómez-Chova L, Amorós J, Camps-Valls G, et alal. Cloud detection for CHRIS/Proba hyperspectral images[J]. Remote Sensing of Clouds and the Atmosphere XRemote Sensing of Clouds and the Atmosphere X. 2005: 59791Q.

    [16] Gómez-Chova L, Camps-Valls G, Amorós-López J, et alet al. New cloud detection algorithm for multispectral and hyperspectral images: Application to ENVISAT/MERIS and PROBA/CHRIS sensors[C]. IEEE International Geoscience and Remote Sensing SymposiumIEEE International Geoscience and Remote Sensing Symposium, IGARSS. 2006: 2757-2760.

    [19] Li P, Dong L, Xiao H, et alet al. A cloud image detection method based on SVM vector machine[J]. NeurocomputingNeurocomputing, 2015, 169: 34-42.

    [20] Latry C, Panem C, Dejean P. Cloud detection with SVM technique[J]. Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International. IEEEGeoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International. IEEE, 2007: 448-451.

    [21] Camarero R, Thiebaut C, Dejean P, et alet al. CNES studies for on-board implementation via HLS tools of a cloud-detection module for selective compression[J]. SPIE Optical Engineering + Applications. International Society for Optics and PhotonicsSPIE Optical Engineering + Applications. International Society for Optics and Photonics, 2010: 781004-781004.

    [22] Shi M, Xie F, Zi Y, et alet al. Cloud detection of remote sensing images by deep learning[J]. Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International. IEEEGeoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International. IEEE, 2016: 701-704.

    [23] Gu Y, Wang S, Shi T, et alet al. Multiple-kernel learning-based unmixing algorithm for estimation of cloud fractions with MODIS and cloudSat data[J]. Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International. IEEEGeoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International. IEEE, 2012: 1785-1788.

    [24] NOAA Satellite Information System (NOAASIS) acquired in 2017-06-29. http://noaasis.noaa.gov/NOAASIS/ml/avhrr.html.

    [26] Bessho K, Date K, Hayashi M, et alet al. An introduction to Himawari-8/9— Japan’s new-generation geostationary meteorological satellites[J]. Journal of the Meteorological Society of JapanJournal of the Meteorological Society of Japan. Ser. II, 2016, 94(2): 151-183.

    [29] Vittorio A V D, Emery W J. An automated, dynamic threshold cloud-masking algorithm for daytime AVHRR images over land[J]. IEEE Transactions on Geoscience & Remote SensingIEEE Transactions on Geoscience & Remote Sensing, 2002, 40(8):1682-1694.

    [33] Gao B-C, Goetz A F H, Wiscombe W J. Cirrus cloud detection from airborne imaging spectrometer data using the 1.38 Μm water vapor band[J]. Geophysical Research LettersGeophysical Research Letters, 1993, 20(4): 301-304.

    [35] Algorithm theoretical basis document for “cloud products” (CMa-PGE01 V3.2, CT-PGE02 v2.2, & CTTH-PGE03 v2.2) acquired in 2017-06-20. http://www.nwcsaf.org/en/web/guest/aemetwebcontents/scientificdocumentation/biasbt/biasbt_v2016_msg1_iodc/pge00p/scientificdocumentationdown_bias_pge00p_msg1_iodc.html#NWCSAF/MSG Basic Documents.

    [37] Rumelhart D E, McClelland J L, Group P R, et alet al. Parallel dDistributed pProcessingParallel distributed processing[J]. IEEE, 1988, 1.

    [38] Srivastava N, Hinton G, Krizhevsky A, et alet al. Dropout: A simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning ResearchJournal of Machine Learning Research, 2014, 15: 1929-1958.

    [39] JAXA Himawari Monitor (P-Tree System) acquired in 2017-10-15. http://www.eorc.jaxa.jp/ptree/.

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    GAO Jun, WANG Kai, TIAN Xiao-Yu, CHEN Jian. A BP-NN based cloud detection method for FY-4 remote sensing images[J]. Journal of Infrared and Millimeter Waves, 2018, 37(4): 477
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