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Remote sensing image registration using SIFT and vegetation index analysis

机译:利用SIFT和植被指数分析进行遥感影像配准

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Aiming at the high accuracy and speed requirements of images registration for multiband data or hyperspectral data, a new method which combines scale invariant feature transform (SIFT) with vegetation index analysis is put forward. Firstly, feature points extracted by SIFT algorithm are classified into two sets - points on vegetation area and points on non-vegetation area, which is based on vegetation index; then the two sets of feature points are matched separately using spectral angle distance as the similarity measure. Transformation parameters are obtained by least square method after mismatched points are removed. Experimental results show that the proposed method achieves higher speed as well as good registration accuracy.
机译:针对多波段数据或高光谱数据对图像配准的高精度和高速度要求,提出了一种将尺度不变特征变换(SIFT)与植被指数分析相结合的新方法。首先,基于植被指数,将SIFT算法提取的特征点分为两类:植被点和非植被点。然后使用光谱角距离作为相似性度量分别匹配两组特征点。去除不匹配点后,通过最小二乘法获得变换参数。实验结果表明,该方法具有较高的速度和良好的配准精度。

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