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31 January 2001 Introducing spatial information in k-means algorithm for cloud detection in optical satellite images
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Due to restricted visibility time of remote sensing polar platforms from earth reception station, on ly a limited number of images can be transmitted. On the case of optical images, an in- board cloud cover detection module will allow to transmit only useful images. In order to derive such a module, we propose a method to detect cloudy areas from subsampled images. For a pixel ground surface of about 110 by 100 m2, cloudy areas appear as the highest radiometric value homogeneous areas. The algorithm presented in this paper is based on the k-means Method. Its main originality is to improve classical results by introducing isotropic spatial information. Input data are the sorted components of a vector composed of radiometric values for each pixel and its neighbors. Then a classical k-means method with constraints on the cloudy class gravity center is used on these vectors. We tested the method on a set of 206 subsampled SPOT XS and 138 SPOT P images and their manmade interpretation masks. To evaluate the quality of our results, we used the probability of false alarm (PFA) depending on the number of pixels which have been wrongly declared cloudy. We obtained rather good PFA and PND, and compared these values with result obtained with other methods.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Laurent Beaudoin, Jean-Marie Nicolas, Florence Tupin, and M. Hueckel "Introducing spatial information in k-means algorithm for cloud detection in optical satellite images", Proc. SPIE 4168, Remote Sensing of Clouds and the Atmosphere V, (31 January 2001);


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