基于迁移学习的低空摄影测量滑坡方量估算方法

针对现有方法难以准确地估算山体滑坡体积的问题，引入人工智能算法，提出耦合迁移学习与微分算法的低空摄影测量山体滑坡方量估算方法。首先，利用SfM与SGM密集匹配等算法从低空无人机立体影像中解算出高精度三维密集点云，结合可见光植被指数和双边滤波算法从密集点云中剥离出目标区地面点云；然后，构建深度神经网络插值模型来表征二维坐标与高程之间的非线性映射关系，并基于参数共享的迁移学习来自适应优化深度神经网络以实现滑坡目标区高程值预测，进而重构滑坡区域的数字地表模型；最后，基于目标区滑坡前后数字地表模型高程差值和微分算法实现山体滑坡方量估算。实验结果表明，该方法平均相对误差为2.7%，相比常用的方法，显著提高了滑坡方量估计精度，并能适应不同地形条件下滑坡方量估算。

Landslide Volume Estimation by Low-Altitude Photogrammetry based on Transfer Learning

The existing methods are difficult to accurately estimate the volume of landslides， to solve this problem， the artificial intelligence algorithm is introduced， and transfer learning and differential algorithms coupled landslide volume estimation by low-altitude photogrammetry is proposed. Firstly， high-precision three-dimensional dense point clouds are derived from low-altitude UAV stereo images by using SfM and SGM dense matching algorithms， and ground point clouds are separated from the dense point clouds by combining visible light vegetation index and bilateral filtering algorithm. Then， a deep neural network for data interpolation is constructed to map the nonlinear relationship between two-dimensional coordinates and elevation information， and the elevation value can be predicted based on the transfer learning of parameter sharing and adaptive optimization， and the digital surface model of landslide area can be reconstructed. Finally， the volume of landslide is estimated based on the elevation difference of the digital surface model before and after the landslide in the target area and the differential algorithm. The experimental results show that the average relative error of the proposed method is approximately equal to 2.7%. Compared with the common methods， the proposed method can significantly improve the accuracy of landslide volume estimation， and is suitable for landslide volume estimation under different terrain.