Landsat TM 遥感影像中厚云和阴影如何去除(6)

Landsat TM 遥感影像中厚云和阴影如何去除

raction method. Because the change of the spectral characteristics caused by cloud shadow is very little, sometimes it is confused with the change caused by land use/cover change. So this paper suggests the combined method of cloud shadow enhancement model and conventional unsupervised classification method to extract cloud shadows. From Fig. 3, the differences between the changes of the spectral characteristics caused by cloud shadows and the other changes in the cloud free region are as follows: First: the reflectance values of the band 4, 5 and 7 are significantly reduced. Second: In cloud shadow regions, the reflectance value of the band 1 is reduced, but in water regions, the reflectance value of the band 1, 2 and 3 is increased. The spectral characteristics of water regions and cloudFig. 5 Enhancement result of cloud and shadow region in August 22, 2007538Journal of Remote Sensing 遥感学报2010, 14(3)cloud shadow regions were extracted. For these result regions, unsupervised classification was performed using Landsat TM original image data. Because each band’s spectral characteristics in the cloud shadow regions are different from the one of vegetation, bare soil and other types of land use/cover category, the cloud shadow regions can be separated from the other land use/cover change regions by using conventional unsupervised classification method. For different cloud thickness, different land use/cover types and different atmospheric conditions, although the extent of such change in spectral characteristics may not be the same, but such change tendency always exists and in the images obtained under better atmospheric conditions, such tendency is more pronounced. Based on this principle, it is possible to obtain cloud free or cloud influence minimized Landsat TM image data using two or more Landsat TM image data of the same period or the same season in different years.And then using the newly calculated auxiliary TM image data which is matched with the main TM image data and cloud and its shadow region enhancement image, pixel replacing process is performed in the cloud influence regions. As a result, the new TM remote sensing image data in which the cloud influence is removed or reduced is obtained. This process is shown in Fig. 6.4RESULTS AND ANALYSISUsing the cloud influence enhancement model CAEM and SAEM described in part 3.2, through the cloud removal process described in part 3.3, the cloud free Landsat TM image data was obtained from the August 22, 2007 Landsat TM image data. Fig. 7 shows the band 5, 4 and 3 color composite image of the result image data. The result shows that cloud and its shadow was removed. Fig.8 is the entire reswlt of the study area.3.3Automatic cloud removal processThe overall process of cloud influence removal or reduction is shown in Fig. 6. For two Landsat TM image data of the same period or the same season in different years, the image registration and image matching operation