Self-Supervised Monocular Depth Estimation Using Global and Local Mixed Multi-Scale Feature Enhancement Network for Low-Altitude UAV Remote Sensing

Estimating depth from a single low-altitude aerial image captured by an Unmanned Aerial System (UAS) has become a recent research focus. This method has a wide range of applications in 3D modeling, digital terrain models, and target detection. Traditional 3D reconstruction requires multiple images, while UAV depth estimation can complete the task with just one image, thus having higher efficiency and lower cost. This study aims to use deep learning to estimate depth from a single UAS low-altitude remote sensing image. We propose a novel global and local mixed multi-scale feature enhancement network for monocular depth estimation in low-altitude remote sensing scenes, which exchanges information between feature maps of different scales during the forward process through convolutional operations while maintaining the maximum scale feature map. At the same time, we propose a Global Scene Attention (GSA) module in the decoder part of the depth network, which can better focus on object edges, distinguish foreground and background in the UAV field of view, and ultimately demonstrate excellent performance. Finally, we design several loss functions for the low-altitude remote sensing field to constrain the network to reach its optimal state. We conducted extensive experiments on public dataset UAVid 2020, and the results show that our method outperforms state-of-the-art methods.

Automated summary

Estimating depth from a single low-altitude aerial image captured by a UAS has become a recent research focus. This study proposes a novel multi-scale feature enhancement network and a global scene attention module for depth estimation in low-altitude remote sensing scenes. The results show that our method outperforms state-of-the-art methods on the UAVid 2020 dataset.