Landsat TM 遥感影像中厚云和阴影如何去除

tribution structure of the land use/ cover is very complex and the block is relatively small in the region. This kind of region is helpful to the researchers who study the effect of the cloud influence removal from Landsat TM image data under the different terrain conditions and different land use/cover types.Fig. 1The color image of Landsat TM (red-band 5, green-band 4, blue-band 3) on August 22, 20072.2Data sourceTwo Landsat TM images of the study area were taken in August of 2006 and 2007 in Landsat 117-33 orbit. These Landsat TM image data were obtained through the network. The remote sensing image data used in this paper were shown in Table 1.Table 1No. 1 2 Satellite Landsat 5 Landsat 5List of used dataDate 2006-08-19 2007-08-22 Sensor TM TMAmong the two remote sensing image data, the one in August 2007 includes relatively large area of locally distributed thick cloud and its shadow regions. Another one in August 2006 includes small area of cloud and its shadow regions. The main purpose of this study is to remove the thick cloud and its shadow. So we use the remote sensing image data in August 2007 as the main data, the remote sensing image data in august 2006 as the auxiliary data. The color composite image of the Landsat TM remote sensing image data in the study area is shown in Fig. 1.Fig. 2 Data preprocessing diagram33.1METHODData pre-processingThe data pre-processing procedure is shown in Fig 2. First, using AutoSync module in ERDAS IMAGINE 9.2, image reg-istration was carried out between the two Landsat TM image data (Dang et al., 2007). Then for the two Landsat TM image data, image match was performed between each corresponding bands. First, the naked eye visual interpretation method is used to select the cloud free region ROI in the two Landsat TM image data. In these regions, for each corresponding bands of the two Landsat TM image data, linear regression analysis was performed. Using linear regression model, each band of the auxiliary data was matched to the corresponding band of the main data. The linear regression model is described as follow.536Journal of Remote Sensing 遥感学报2010, 14(3)Yi = aiXi + bi (1) where Xi: the i band’s gray value of the auxiliary data; Yi: the i band’s transformed gray value; i: the band number. Table 2 shows that the correlation between each corresponding bands of the two Landsat TM image data is very high. In particular, the pairs of band 4 and band 5 have the maximum value respectively.Table 2Band 1 2 3 4 5 7Linear regression resultsa 0.86 0.86 0.82 0.92 0.94 0.86 b 7.75 3.53 3.51 0.52 0.02 1.70 R 0.90 0.87 0.87 0.96 0.96 0.933.2Cloud and shadow enhancement model3.2.1 Spectral characteristics of the thick cloud region To extract the thick cloud and its shadow region, the comparison analysis of the spectral characteristics between cloud free region and cloud region was performed in this paper. Fig. 3 is the 5, 4, 3 bands color composite image