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.


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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

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