Ren Chaofeng, Zhi Xiaodong, Pu Yuchi, Zhang Fuqiang
College of Geological Engineering and Geomatics, Changoan University, Xioan 710054, China.
eFly of China Electronic Science Technology Co., Ltd., Chongqing 401332, China.
Math Biosci Eng. 2021 Mar 5;18(3):2274-2287. doi: 10.3934/mbe.2021115.
Three-dimensional (3D) sparse reconstruction of landslide topography based on unmanned aerial vehicle (UAV) images has been widely used for landslide monitoring and geomorphological analysis. In order to solve the isolated island phenomenon caused by multi-scale image matching, which means that there is no connection between the images of different scales, we herein propose a method that selects UAV image pairs based on image retrieval. In this method, sparse reconstruction was obtained via the sequential structure-from-motion (SfM) pipeline. First, principal component analysis (PCA) was used to reduce high-dimensional features to low-dimensional features to improve the efficiency of retrieval vocabulary construction. Second, by calculating the query depth threshold and discarding the invalid image pairs, we improved the efficiency of image matching. Third, the connected network of the dataset was constructed based on the initial matching of image pairs. The lost multi-scale image pairs were identified and matched through the image query between the connection components, which further improved the integrity of image matching. Our experimental results show that, compared with the traditional image retrieval method, the efficiency of the proposed method is improved by 25.9%.
基于无人机（UAV）图像的滑坡地形三维（3D）稀疏重建已广泛应用于滑坡监测和地貌分析。为了解决多尺度图像匹配导致的孤岛现象，即不同尺度的图像之间没有连接，我们在此提出一种基于图像检索选择无人机图像对的方法。在该方法中，通过顺序运动恢复结构（SfM）管道获得稀疏重建。首先，使用主成分分析（PCA）将高维特征降为低维特征，以提高检索词汇构建的效率。其次，通过计算查询深度阈值并丢弃无效图像对，提高了图像匹配的效率。第三，基于图像对的初始匹配构建数据集的连接网络。通过连接组件之间的图像查询识别并匹配丢失的多尺度图像对，进一步提高了图像匹配的完整性。我们的实验结果表明，与传统图像检索方法相比，该方法的效率提高了25.9%。
