Journal of Systems Engineering and Electronics

• SOFTWARE ALGORITHM AND SIMULATION • Previous Articles     Next Articles

Cloud removal of remote sensing image based on multi-output support vector regression

Gensheng Hu1,2, Xiaoqi Sun2, Dong Liang1,2,*, and Yingying Sun2   

  1. 1. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China;
    2. School of Electronics and Information Engineering, Anhui University, Hefei 230601, China
  • Online:2014-12-29 Published:2010-01-03


Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing
coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.