Automatic cloud detection for high resolution satellite stereo images and its application in terrain extraction

The automatic extraction of terrain from high-resolution satellite optical images is very difficult under cloudy conditions. Therefore, accurate cloud detection is necessary to fully use the cloud-free parts of images for terrain extraction. This paper addresses automated cloud detection by introducing an image matching based method under a stereo vision framework, and the optimization usage of non-cloudy areas in stereo matching and the generation of digital surface models (DSMs). Given that clouds are often separated from the terrain surface, cloudy areas are extracted by integrating dense matching DSM, worldwide digital elevation model (DEM) (i.e., shuttle radar topography mission (SRTM)) and gray information from the images. This process consists of the following steps: an image based DSM is firstly generated through a multiple primitive multi-image matcher. Once it is aligned with the reference DEM based on common features, places with significant height differences between the DSM and the DEM will suggest the potential cloud covers. Detecting cloud at these places in the images then enables precise cloud delineation. In the final step, elevations of the reference DEM within the cloud covers are assigned to the corresponding region of the DSM to generate a cloud-free DEM. The proposed approach is evaluated with the panchromatic images of the Tianhui satellite and has been successfully used in its daily operation. The cloud detection accuracy for images without snow is as high as 95%. Experimental results demonstrate that the proposed method can significantly improve the usage of the cloudy panchromatic satellite images for terrain extraction.