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Cloud Classification in JPEG-compressed Remote Sensing Data (LANDSAT 7/ETM+)

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Computational Science and Its Applications – ICCSA 2012 (ICCSA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7334))

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Abstract

Environmental parameters required for geo-information modelling are subject to spatial and temporal dynamics. Remote sensing data can contribute to measure those parameters. For that purpose high-accuracy classifications of remote sensing data are required which can be very time-consuming due to the large data volumes involved. In many applications, however, the rapid provision of classified mass data is of higher priority than classification accuracy. One important focus on research and development efforts in the past years has been to optimise the automated interpretation of remote sensing data. Different investigators have shown that this interpretation can both be effective and efficient in JPEG compressed data with acceptable accuracy. This paper presents an operational processing chain for cloud detection in JPEG-compressed quick-look products of LANDSAT 7/ETM+-scenes (compression ratio is 10:1). Two well-developed conventional algorithms are applied to these datasets for cloud detection. Results show that the processing chain developed is stable and produces quality results with substantially compressed mass data.

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References

  1. Lam, K.W.-K., Lau, W.-L., Li, Z.-L.: Effects of JPEG Compression on Accuracy of Image Classification. In: Proceedings of GIS Developments, ACRS, p. 3 (1999), http://www.gisdevelopment.net/aars/acrs/1999/ts11/ts11009.shtml (last access: August 13, 2005)

  2. Lau, W.-L., Li, Z.-L., Lam, K.W.-K.: Effects of JPEG compression on image classification. Int. J. Remote Sensing 24(7), 1535–1544 (2003)

    Article  Google Scholar 

  3. Zabala, A., Pons, X., Masó, J., García, F., Aulí, F., Serra, J.: Evaluation of JPEG and JPEG2000 effects on remote sensing image classification for mapping natural areas. WSEAS Transactions on Information Science and Applications 6(2), 717–725 (2005)

    Google Scholar 

  4. Zabala, A., Pons, X., Diaz-Delgado, R., García-Vílchez, F., Aulí, F., Serra-Sagristà, J.: Effects of JPEG and JPEG2000 lossy compression on remote sensing image classification for mapping crops and forest areas. In: 26th International Geoscience and Remote Sensing Symposium. IEEE Press, Denver (2006)

    Google Scholar 

  5. Spaventa, V.D.: Personal communication (2004)

    Google Scholar 

  6. Lammi, J., Sarjakoski, T.: Image Compression by the JPEG algorithm. Photogrammetric Engineering and Remote Sensing 61, 1261–1266 (1995)

    Google Scholar 

  7. Lane, T.: JPEG image compression FAQ, part 1 and part 2 (1999), http://www.faqs.org/faqs/jpeg-faq/ (last access: February 20, 2012)

  8. Irish, R.: Automatic Cloud Cover Assessment (ACCA) LANDSAT 7 ACCA. Goddard Space Flight Center. LANDSAT -7 Science Team Meeting (December 1-3, 1998), http://landsathandbook.gsfc.nasa.gov/pdfs/ACCA_slides.pdf (last access: February 20, 2012)

  9. Irish, R.: LANDSAT 7 Automatic Cloud Cover Assessment. In: Sylvia, S.S., Descour, M.R. (eds.) Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI. Proceedings of SPIE, vol. 4049, pp. 348–355 (2000)

    Google Scholar 

  10. Xu, Q., Wu, W.: ACRES Automatic Cloud Cover Assessment of LANDSAT 7 Images. In: Spatial Sciences Conference 2003 – Spatial Knowledge without Boundaries, Canberra, September 23-26, p. 10 (2003)

    Google Scholar 

  11. Slater, P.N., Biggar, S.F., Holm, R.G., Jackson, R.D., Mao, Y., Moran, M.S., Palmer, J.M., Yuan, B.: Reflectance and Radiance-Based Methods for the In-Flight Absolute Calibration of Multispectral Sensors. Remote Sens. Environ. 22(1), 11–37 (1987)

    Article  Google Scholar 

  12. NASA (2011), http://landsathandbook.gsfc.nasa.gov/cpf/prog_sect9_2.html (last access: January 04, 2012)

  13. Markham, B.L., Barker, J.L.: Spectral characterization of the LANDSAT Thematic Mapper Sensors. Int. J. Remote Sensing 6(5), 697–716 (1985)

    Article  Google Scholar 

  14. Chander, G., Markham, B.L., Helder, D.L.: Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors. Remote Sens. Environ. 113, 893–903 (2009), http://landportal.gsfc.nasa.gov/Documents/Landsat_Calibration_Summary.pdf (last access: February 20, 2012)

    Article  Google Scholar 

  15. Gurney, R.J., Hall, D.K.: Satellite-derived surface energy balance estimates in the Alaskan Sub-Arctic. J. Clim. Appl. Meteor. 22(1), 115–125 (1983)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. German Aerospace Center, German Remote Sensing Data Center, 17235, Neustrelitz, Germany

    Erik Borg & Bernd Fichtelmann

  2. Department of Geography, Potsdam University, 14476, Potsdam, Germany

    Hartmut Asche

Authors
  1. Erik Borg
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  2. Bernd Fichtelmann
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  3. Hartmut Asche
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Editor information

Editors and Affiliations

  1. Laboratory of Urban and Territorial Systems, University of Basilicata, 10, Viale dell’Ateneo Lucano, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli 1, 06123, Perugia, Italy

    Osvaldo Gervasi

  3. Department of Cyber Security Science, Federal University of Technology, Gidan Kwano Campus, Minna, Nigeria

    Sanjay Misra

  4. Faculty of Engineering, Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rua Sao Francisco Xavier, 524, 50. andar, sala 5145-F, Maracana, 20.550-013, Rio de Janeiro, RJ, Brazil

    Nadia Nedjah

  5. Department of Production and Systems, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha

  6. School of Business Systems, Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, 813-8503, Higashi-ku, Fukuoka, Japan

    Bernady O. Apduhan

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© 2012 Springer-Verlag Berlin Heidelberg

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Borg, E., Fichtelmann, B., Asche, H. (2012). Cloud Classification in JPEG-compressed Remote Sensing Data (LANDSAT 7/ETM+). In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31075-1_26

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  • DOI: https://doi.org/10.1007/978-3-642-31075-1_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31074-4

  • Online ISBN: 978-3-642-31075-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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