矿区塌陷区遥感影像改进自适应维纳滤波算法

遥感影像为矿区开采沉陷研究提供了大量可靠的数据，对于提高矿区开采沉陷监测与预计的精度具有重要作用，但由于遥感影像的获取受到矿区成像环境、成像器件固有的缺陷等因素的影响，易混入不同程度的随机噪声，降低了遥感影像的成像质量，导致难以高精度提取矿区塌陷区域的相关数据。为此，提出了一种针对矿区塌陷区遥感影像的滤波算法。该算法首先对自适应维纳滤波算法添加了噪声图像块检测环节，对其进行了适当改进，将改进后的自适应维纳滤波算法用于对遥感影像进行去噪；然后针对去噪后遥感影像对比度不高的问题，采用动态均值算法进行增强处理，即通过设定某一阈值，将遥感影像像素点灰度值划分为亮度异常和亮度正常2个部分，采用亮度正常的像素点灰度值修正亮度异常的像素点灰度值，实现对遥感影像对比度的动态调整。采用一幅某矿区塌陷区的遥感影像分别对新算法、自适应维纳滤波、中值滤波、非局部均值滤波等算法进行试验，结果表明：新算法对于矿区塌陷区遥感影像的滤波效果相对于其余算法而言有一定的优势，对于提高矿区开采沉陷监测与预计的精度有一定的帮助。

Improved Adaptive Wiener Filtering Algorithm of the Remote Sensing Image of Mining Subsidence Area

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 ar-ea.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 algo-rithm 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 refer-ence to improve the precise of mining subsidence monitoring and prediction in mining area.