A new thin cloud removal algorithm in single airborne image

The application of high-resolution airborne images becomes more and more extensive. However because of the complexity of atmospheric environment, airborne remote sensing imaging process is easily affected by cloud and mist, which results in airborne image blurred or loss of information. So it is a necessary task to remove effects of cloud to get clearer images before the next application such as image registration. This paper proposes a new method of removing thin cloud cover from single airborne image. This method applies scale space transform to get scale space sequence images. Then we use difference between different levels to extract cloud area. Next, we use gray classification which represents cloud effect degree in the highest level of cloud area. Finally, we use the original image filtered by Laplacian to subtract the last step result. Compared with other thin cloud cover removal methods which include the homomorphic filtering method, wavelet transform method and mathematical morphology by visual evaluation and statistical analysis, the method proposed by this paper proves to be the most efficient way in the processing of thin cloud cover of airborne image.