A Fast Cloud Detection Approach by Integration of Image Segmentation and Support Vector Machine

  • Bo Han
  • Lishan Kang
  • Huazhu Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


We proposed a fast cloud detection approach for the geophysical data from Moderate Resolution Imaging Spectroradiometer (MODIS), a premium instrument aboard on NASA’s satellite Terra to study clouds and aerosols. Previous pixel-based classifiers have been developed for remote-sensing instruments using various machine learning techniques, such as artificial neural networks (ANNs), support vector machines (SVMs). However, their computational costs are very expensive. Our novel approach integrated image segmentation and SVMs together to achieve the similar classification accuracy while using much less computation costs. It exploited the homogeneous property in local spatial sub-regions and used radiance information from sub-regions, rather than pixels, to build classifiers. The experimental results showed the proposed approach not only greatly speed up the classification training procedure, but also provide insights for domain experts to reveal different cloud types.


Support Vector Machine Image Segmentation Cloud Type Moderate Resolution Image Spectroradiometer Cloud Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ackerman, S.A., Strabala, K.I., Menzel, W.P., Frey, R.A., Moeller, C.C., Gumley, L.E.: Discriminating Clear-sky from Clouds with MODIS. J. of Geophy. RES-ATMOS 103, 32141–32157 (1998)CrossRefGoogle Scholar
  2. 2.
    Azimi-Sadjadi, M.R., Zekavat, S.A.: Cloud Classification Using Support Vector Machines. In: Proceedings of the 2000 IEEE Geosci. and Remote Sensing Symp., vol. 2, pp. 669–671 (2000)Google Scholar
  3. 3.
    Bankert, R.: Naval Research Laboratory Monterey GOES Cloud Classification (website) (2005),
  4. 4.
    Han, B., Vucetic, S., Braverman, A., Obradovic, Z.: Construction of an Accurate Geospatial Predictby Fusion of Global and Local Models. In: 8th Int. Conf. on Information Fusion (2005)Google Scholar
  5. 5.
    Lee, Y., Wahba, G., Ackerman, S.: Cloud Classification of Satellite Radiance Data by Multicategory Support Vector Machines. J. of Atmos. and Oceanic Tech. 21, 159–169 (2004)MATHCrossRefGoogle Scholar
  6. 6.
    Li, J., Menzel, W.P., Yang, Z., Frey, R.A., Ackerman, S.A.: High-spatial-resolution Surface and Cloud-type Classification from MODIS Multispectral Band Measurements. J. of Appl. Meteorology 42, 204–226 (2003)CrossRefGoogle Scholar
  7. 7.
    Mazzoni, A.H., Garay, M., Tang, B., Davies, R.: A MISR Cloud-type Classifier Using Reduced Support Vector Machines. In: Proceedings of the Eighth Workshop on Mining Scientific and Engineering Datasets. SIAM Int. Conf. on Data Mining (2005)Google Scholar
  8. 8.
    Tian, B., Azimi-Sadjadi, M.R., Vonder Haar, T.H., Reinke, D.: Temporal Updating Scheme for Probabilistic Neural Network with Application to Satellite Cloud Classification. IEEE Trans. on Neural Networks 11, 903–920 (2000)CrossRefGoogle Scholar
  9. 9.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bo Han
    • 1
    • 2
  • Lishan Kang
    • 1
  • Huazhu Song
    • 3
  1. 1.School of Computer ScienceWuhan UniversityWuhanP.R. China
  2. 2.Department of Computer and Information ScienceTemple UniversityPhiladelphiaUSA
  3. 3.School of Computer Science and TechnologyWuhan University of TechnologyWuhanP.R. China

Personalised recommendations