基于高分遥感影像匹配的露天矿滑坡形变场监测方法

针对利用SIFT(Scale-invariant feature transform)算法提取全色遥感影像特征点时存在数量少、分布不均、影响滑坡形变场监测效果等问题,提出了一种综合利用SIFT与CSIFT(Colored scale invariant feature transform)算法进行滑坡形变场标定的方法。该方法利用SIFT算法分别处理全色影像和多光谱融合影像,利用CSIFT算法处理多光谱融合影像,将3个过程提取的互不相同的特征点进行叠加,得到更多的特征点。以抚顺西露天煤矿南帮滑坡为例进行试验,结果表明:新方法能够充分利用3种不同影像匹配过程的特点,大大增加了特征点数量,使得生成的滑坡形变场更加精确,所圈定的滑坡范围更加准确,且该方法具有成本低、效率高等优点,非常适合于大型滑坡的大变形形变场监测。

Monitoring Method of Landslide Deformation Field Based on High Resolution Remote Sensing Images Matching

The feature points of panchromatic remote sensing images can be extracted by using SIFT (scale-invariant feature transform) algorithm，but the feature points is characterized by small quantity and uneven distribution，besides that，the monitoring effects of landslide deformation field is influenced to some extent.In order to improve the monitoring precise of landslide deformation field，a new method of calibrating landslide deformation field based on SIFT algorithm and CSIFT (colored scale invariant feature transform) algorithm is proposed.According to the principle of the new method，the panchromatic image and multispectral fusion image are processed by using SIFT algorithm respectively，the multispectral fusion image is processed by using CSIFT algorithm，the different feature points extracted by the above processes are superimposed，so as to obtain more feature points.Taking the slope of the south side of Fushun West open-pit coal mine as an example，the test results show that the new method can fully utilize the characteristics of the three different image matching processes，the quantity of feature points are increased greatly，which make the generated landslide deformation field more detailed and accurate，and the extent of landslide more accurate.The study result further show that the new method is characterized by higher degree of automation and lower cost，therefore，it is very suitable for monitoring large deformation field of large-scale landslide.