利用Harris-Laplace和SIFT描述子进行低空遥感影像匹配

Matching Low Altitude RS Image with Harris-Laplace and SIFT Descriptor

提出了基于Harris-Laplace和SIFT描述子的改进的特征匹配方法。在特征点检测阶段,采用Har-ris-Laplace算法检测出影像上的关键点,该关键点对光照变化、图像噪声和尺度变化具有不变性;然后,确定关键点的主方向,生成特征点。在特征点描述阶段,采用SIFT描述子对特征点进行描述;在特征点匹配阶段则利用BBF算法和RANSAC算法对特征点进行粗匹配和精匹配。实验结果表明,相对于基于SIFT的匹配方法,此算法在匹配速度相同的情况下,提高了匹配精度。

An improved feature matching method based on Harris-Laplace and SIFT descriptor is proposed.Because of the instability of low altitude remote sensing platform,the difference of spin deflection angle and scale between low altitude remote sensing imageries is great.The results obtained by matching method based on area grayscale can＇t meet the real requirements.The feature points detected by SIFT algorithm are easily affected by image noise and slight texture change.With the proposed method,Harris-Laplace is used to detect key points of the image,which are invariant to illumination change,image noise and scale change.And then the orientation of these key points is determined to form feature points from these key points.These feature points are described by SIFT descriptor,and matched using BBF algorithm and RANSAC algorithm.One experiment is introduced,which uses low altitude remote sensing image with high resolution as input data.The experimental results show that the proposed method possesses higher matching accuracy at the same matching speed compared with the matching method based on SIFT algorithm.