云一直是遥感图象处理、图象分析的一大障碍.为了解决这一问题,试图探讨利用中分辨率成像光谱仪MODIS检测云的方法,该方法充分考虑到MODIS数据具有36个光谱通道,特别是红外波段细分的特点,先是基于云的波谱特性采用多光谱综合法、红外差值法及指数法来对MODIS图象上的云点进行检测,鉴于这些方法有一定的局限性,因而还运用了一种基于空间结构分析和神经网络的云自动检测算法;最后将各种方法的云检测结果进行相互映证和对照分析,结果表明,这些方法检测到的云互相吻合,说明利用MODIS图象可成功地检测云点像元.这不仅为云的去除奠定了良好基础,而且也可以提高图象识别、图象分类及图象反演的精度.

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.