Abstract
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.
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Han, B., Kang, L., Song, H. (2006). A Fast Cloud Detection Approach by Integration of Image Segmentation and Support Vector Machine. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_176
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DOI: https://doi.org/10.1007/11760191_176
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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