Satellite images are useful for creating updated land cover maps. But the major problem in these images is that the region below the clouds are not covered by the sensor. Hence cloud detection and removal is very vital in the processing of satellite imagery. The objective of this study is to propose an approach for automatic detection and removal of cloud and its shadow contamination from Satellite Images. After detection and removal of the contamination the method will selectively replace the data from different images of the same area to minimize the cloud contamination effect. Detection is achieved by performing two cloud segmentation algorithms namely Average brightness thresholding (ABT) algorithm and Region growing algorithm. Finally the performance of the two algorithms are compared to detect the exact cloud region. This is followed by the detection of the corresponding shadow pair. Finally the detected cloud contamination is removed and replaced with the data from different images of the same area. The algorithms were tested using multispectral ASTER(Advanced Space Borne Thermal Emission and Reflection Radiometer) and LISS III data. The procedure is computationally efficient and hence could be very useful in providing improved weather forecast, land cover and analysis products.
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