Landsat TM 遥感影像中厚云和阴影如何去除(4)

Landsat TM 遥感影像中厚云和阴影如何去除

of the Landsat TM image data on August 22, 2007 (a) and spatial profile curve (b). In Fig. 3(a), the straight line lies across the region which includes vegetation, water, thick cloud and its shadows. In the color composite image, the white region is the thick cloud region and the black part beside it is its shadow region. From Fig. 3, the spectral characteristics of cloud and its shadow region are as follows: (1) In all bands, the spectral reflectance value in thick cloud region is significantly higher than cloud free region (The difference is over 150). (2) In water bodies and cloud shadow regions, the spectral reflectance value of band 4, 5 and 7 is significantly reduced.(3) In cloud shadow regions, the spectral reflectance value of band 1, 2 and 3 is reduced a little. But in water bodies, the spectral reflectance value of band 1, 2 and 3 is increased. Although the variation amount of the spectral characteristics is not the same with the difference of cloud thickness, but such variation trend caused by thick cloud and its shadow is the same. 3.2.2 Cloud enhancement model Based on the analysis result above, we proposed the cloud and its shadow region enhancement model. The change content of the spectral characteristics between two TM image data includes the change part caused by cloud and the change part caused by land use/cover change. So, in order to extract cloud and its shadow region, first of all, it is necessary to distinguish between the change part of the spectral characteristics caused by cloud and the one caused by land use/cover change. Firstly, the thick cloud enhancement model is designed in this paper. From Fig. 3, the differences between the change of the spectral characteristics caused by thick cloud and the one caused by the land use/cover change are as follows: First: the change of the spectral characteristics caused by thick cloud is very great. The variation of the spectral reflectance values of each band is more than 150. Second: the change trend of the spectral characteristics of each band is the same, which is increased. Based on the above two features, thick cloud enhancement model is designed as follow. CAEM = MD×CDF (2)∑ BRefi BAuxiMD =ni =1nn(3) (4)CDF=Sign[ ∑ Sign( BRefi BAuxi ) n + 1 ]i =1where BRefi: the i band’s gray value of the main data; BAuxi: the i band’s gray value of the auxiliary data In this paper, the Landsat TM image data on August 22, 2007 was used as main data and the one at August 19, 2006 which was transformed by linear regression model was used as auxiliary data. CAEM: Cloud area enhancement model; MD: Mean absolute difference; CDF: Cloud discriminate function; n: the number of bands. If the gray value of all bands of the main TM data is greater than the one of the auxiliary data, CDF = 1; otherwise, CDF≤0. In the region in which there is a great change of the spectral characteristics, MD value is high, but in the region in which there is no change of the sp