Landsat TM 遥感影像中厚云和阴影如何去除

is performed. In the cloud free regions, for each pair of the corresponding bands of two Landsat TM image data (one is a main data and another one is an auxiliary data), linear regression analysis is performed and as a result, the new auxiliary TM image data matched with main data is obtained. It can reduce the influence caused by different sun position, different atmospheric conditions and some other conditions. Using the above cloud enhancement model (CAEM) and cloud shadow enhancement model (SAEM), using unsuper-vised classification method and using the Modeler module, the programming Model Maker, in ERDAS IMAGINE 9.2, the cloud and its shadow region enhancement image can be calculated from the original TM image dataFig. 7 Cloud free TM image Fig. 6 Diagram of automatic cloud removal(a) Cloud and shadow region; (b) Cloud free imageRI Pyongsop et al.: Cloud and shadow removal from Landsat TM data539The content of the non-shadow region among the shadow enhancement image calculated using SAEM model is as follows: (1) paddy regions, specifically, in the 2006 year TM data (auxiliary data), rice was already planted, but in the 2007 year TM data (main data) rice was not planted yet in paddy fields. It is similar to a paddy in water region. Such effect is rather good for the automatic classification of paddy fields because its nature is not water, just paddy; (2) tideland region and water bodies; depending on the water elevation change, some regions may be enhanced as shadow. Through unsupervised classification, such regions can be distinguished with shadow regions.the Korean Peninsula. The result shows that: The above method can effectively remove or weaken the cloud influence and the process is relatively simple. Moreover, in the process, it does not need any strict threshold value adjustment. It needs only the spectral characteristics of the Landsat TM image data. It does not need any other information. So, for another regions, another times Landsat TM image data, the above-mentioned process can be automatically applied. Although this study used only two Landsat TM image data to remove cloud influence, but in order to obtain better result, it can be used more than two Landsat TM image data. Above-mentioned operation is performed in each pixel as a unit, the requirement of an image registration is very strict and in order to obtain better result, it needs high-precision topographic correction, atmospheric correction and image matching operation.REFERENCESDang A R, Wang X D, Chen X F and Zhang J B. 2007. ERDAS IMAGINE remote sensing image processing methods. Beijing: Qinghua University Press Li X and Ye J A. 1997. The accuracy improvement of the land use change monitoring based on remote sensing using principal component analysis. Journal of Remote Sensing, 1(4): 282—288 Song X N and Zhao Y S. 2003. Cloud detection and analysis in MODIS images. Chinese Journal of Image and Graphics, 8(9): 1079— 1083 Song X Y, Liu L Y, Li C J, Wang