Abstract: A large number reliable data are provided for the research of mining subsidence by remote sensing image，it plays an important role to improve the precise of mining subsidence monitoring and prediction.Due to the existing influence factors such as the imaging environment of mining area,the inherent defects of imaging devices and so on,the imaging quality of remote sensing image is reduced,which increased the difficulties to extract the relevant data of the mining subsidence area with high precise,so,it is necessary to conduct pre-processing of the remote sensing obtained in specific mining subsidence area.A new filtering algorithm of the remote sensing image of mining subsidence area is proposed,firstly,the noise image block detection method is proposed to improve the adaptive Wiener filtering algorithm,the improved adaptive Wiener filtering algorithm is proposed to deal with the noise remote sensing image of mining subsidence area;secondly,according to the low contrast of the filtering remote sensing image of mining subsidence area,the dynamic mean algorithm is adopted to conduct enhancement processing of it,to be specific,the pixel gray values of the remote sensing image of mining subsidence area are divided into two parts (abnormal brightness and normal brightness),the abnormal brightness pixel gray values are corrected by the normal brightness pixel gray values by setting a threshold so as to conduct dynamic adjustment of the remote sensing image contrast.The remote sensing of the mining subsidence area of a mine are obtained as the experimental data,the adaptive Wiener filtering algorithm,median filtering algorithm,non-local means filtering algorithm and the algorithm proposed in this paper are conducted experimental comparison and analysis,the results show that the performance of the algorithm proposed in this paper is superior to the adaptive Wiener filtering algorithm,median filtering algorithm and non-local means filtering algorithm,it has some reference to improve the precise of mining subsidence monitoring and prediction in mining area.

Key words: Mining subsidence, Remote sensing image, Adaptive Wiener filtering, Noise image block, Dynamic mean algorithm, Median filtering, Non-local means filtering