面向海洋应用的无人机遥感图像配准研究

Remote Sensing Image Registration of Unmanned Aerial Vehicle for Marine Applications

在海洋应用中,大面积水体的同名点匹配相比陆地更加困难,制约了无人机遥感图像的配准精度和收敛速度。本文提出了一种改进算法适用于海洋无人机遥感应用,采用主成分分析(PCA)和水体阈值方法去除水体,获得图像中非水体区域的分块图像,然后利用仿射-尺度不变特征变换算法(ASIFT)进行图像的特征点提取和重叠图像非水体区域的同名点匹配。通过海岛、海岸线的无人机遥感试验结果表明,基于改进算法,在不增加时间开销的情况下,可以增加30%~50%的同名点数量,精度提高约5%~10%。文中方法适应用于海洋无人机遥感的序列图像配准,为海岛、海岸线的遥感监测提供了有效的技术支持。

In marine applications,large water areas lead to more difficulties compared to land on the corresponding point matching,which restricts the UAV remote sensing image registration precision and convergence speed. This paper proposes an improved algorithm for the marine UAV remote sensing applications. First,remove water using principal component analysis（PCA） and water threshold method,and obtain the block image without water. Then,feature points extraction and the corresponding points matching of these block images were performed using the affine scale invariant feature transform algorithm（Affine-Sift,ASIFT）. The experiment results show that the improved algorithm can increase 30% ~ 50% points,the accuracy increases by about 20%,and time is not increased. This method is suitable for the sequence of UAV remote sensing image registration on the marine,and it can provide effective technical support for the remote sensing monitoring of island and coastline.