The nodus of remote sensing monitoring for fog is the separation of cloud and fog.Synthetically using the ground-object spectral information and the image-textural information

together with the applications of fractal theory and BP neural network

have increased the reliability of the cloud and fog separation result.The image-textural extraction basing on gray scale connected region has improved the recognising ability of the cloud and fog boundary

while the fractional dimension weighted by gray scale has improved the separability of cloud and fog.Compared with the traditional Maximum Likelihood Classification

the identified precision of clear sky ground

fog areas

cloud areas was increased

especially that of the cloud areas was increased by 10%

and that of the three kind objects in total was increased by 7%

so it was more than 93%.The merging of these kinds was discussed also.