MODIS (Moderate Resolution Imaging Spectroradiometer) is a kind of new weather satellite data. Few weather satellite images obtained are all clear sky and they are always influenced by cloud more or less. Cloud is a large obstacle to remote sensing image processing and analysis all the while. In order to extract objective information more effective

cloud should be removed from the remote sensing images

which is an essential sector in the image preprocessing. Cloud detection is the most important processing before removing cloud. Taking it into account that MODIS data includes thirty-six bands

especially the infrared channels subdivided

it has realized cloud detection in MODIS images by multi-spectral synthesis method

infrared difference algorithm and cloud detection index in this paper. Owing to the limitation to a certainty of the above methods

an automatic cloud detection algorithm is applied based on the spatial texture analysis and neural network in this research. At last the cloud detection results gained by different ways are testified each other and analyzed by comparison. It found that the results are consistent

which shows that the cloud-contaminate pixels are detected successfully. It not only lays a good foundation for the cloud removing

but also can improve the precision of remote sensing image recognition

classification and inverse in this study.