Landsat TM 遥感影像中厚云和阴影如何去除

ectral characteristics, MD value is low. Among the region in which MD value is high, if CDF = 1, this region is just the cloud region. In order to reduce the influence of the variation of sun’s position, different atmospheric conditions and a number of otherFig. 3 Spectral characteristics of cloud region(a) Color image of band 5, 4, 3; (b) Profile curves of the interest regionRI Pyongsop et al.: Cloud and shadow removal from Landsat TM data537reasons, the auxiliary data was matched to the main TM image data. For each corresponding band of the two TM image data, the linear regression analysis was performed in the cloud-free region. Using this linear regression model, the auxiliary TM image data was transformed. Fig. 4 is the cloud region enhancement result calculated by the above model. Fig. 4 shows that the difference between thick cloud region and other region is very clear. Using a simple threshold, we can distinguish thick cloud regions and other regions; moreover the range of the usable thresholds is relatively big.shadow regions in Landsat TM image data are similar. So, in order to extract cloud shadow regions, firstly water region must be extracted. Based on the above change features of the spectral characteristics, the cloud shadow enhancement model is designed. SAEM = SDF×[(BAux5 BRef5)+(BAux7 BRef7)]/2 SDF=Sign(1 Sign(∑ Sign( BRefi BAuxi ) 2))i =1 3(5) (6)where BRef i: the i band’s gray value of the main data; BAuxi:: the i band’s gray value of the auxiliary data; SAEM: Shadow area enhancement model; SDF: Shadow discriminate function; Sign(): the sign function which extracts the sign of a real number. If the values of the band 1, 2 and 3 of the main TM data are higher than the one of the auxiliary data, SDF = 0 (it is water region); otherwise, SDF≥1 (it is not water region). Among the region in which the reflectance values of the band 5 and band 7 of the main TM data are reduced, if it is not water region (SDF≥1), SAEM value would be high. The cloud shadow enhancement image calculated using the above model also contains the spectral characteristics change region caused by land use/cover change. In Fig. 5, the pink regions are thick cloud regions and the green regions are likely cloud shadow regions. In order to distinguish between cloud shadow regions and the spectral characteristics change regions caused by land use/cover change, this paper used unsupervised automatic classification method. For the result image calculated using the SAEM model, the threshold operation was performed and as a result, the likelyFig. 4 Enhancement result of cloud regionsIn this paper, two threshold values of 30 and 50 were used to compare the two result images and analyze the influence of selection of different threshold value. The analysis result shows that the change between two result images is less than 0.05% of the entire image area. Cloud shadow extraction method is more complex than the thick cloud ext