The combination of band ratioing techniques and neural networks algorithms for MSG SEVIRI and Landsat ETM+ cloud masking

In this paper a new approach from the combination of band ratioing function and MLP Neural Networks technique is proposed to differentiate between clouds and background in Landsat ETM+ and MSG SEVIRI data. First, in order to increase the contrast of the clouds and background, a band ratioing function is applied to each sub-image. Second, the sub-images are segmented by MLP Neural Networks technique. The proposed approach was tested on 40 Landsat ETM+ sub-images of Gulf of Mexico and on 40 MSG SEVIRI sub-images over Italy. The same parameters were used in all tests. For the overall dataset, the average accuracy of 89 % was obtained for Landsat ETM+ images and the average accuracy of 85 % was obtained for MSG SEVIRI images. Our experimental results demonstrate that the proposed approach is robust and effective.