Cloud Detection Algorithm for LandSat 8 Image Using Multispectral Rules and Spatial Variability

Abstract

Since 1972, LandSat program has experienced six successful missions that have contributed to nearly 40 years record of Earth Observations for monitoring the land cover and change dynamics. LandSat images have provided new dataset for field monitoring with geospatial information at high spatial resolution and become one of the most widely used sources of satellite images in various domains. The LandSat 8 generation launched in February 2013 continues the mission of collecting images of the Earth with an open and free data policy. However, clouds are often obscure the detection of land surface and restrict the analysis. The paper proposes improvements for an effective method of cloud detection that widely used in LandSat 5-7 in order to obtain better results for the new satellite generation LandSat 8’s images. The validation demonstrates that cloud and cloud contaminated pixels were almost detected with overall recall, precision, accuracy and error of over 98.59, 99.94, 99.89 and 0.11%, respectively on testing datasets. In comparison with the original method, our approach is able to detect 13.64% more cloud pixels with a lower error of 1.03% and 1.04 % higher accuracy rate.