Aerial orthoimage generation for UAV remote sensing: Review

Abstract

With its unique advantages of high flexibility and high efficiency, UAV has become a reasonable substitute for conventional aerial measurement technology. Especially in the low altitude remote sensing image processing, the ortho-rectification and mosaic of aerial images are the key to vision-based UAV orthoimage generation. Therefore, how to select the appropriate methods to rectify and mosaic the aerial images of UAV is significance to research the automatic generation of digital orthoimages. Unfortunately, most of the existing reviews only focus on the general image mosaic techniques, and there are few special reports on the application of UAV orthoimage generation for reference. This paper presents a comprehensive survey on UAV orthoimage generation technologies. We conclude three mainstream frameworks of visual orthoimage generation, which are 2D mosaic framework based, SfM framework based and SLAM framework based methods. According to the above three specific frameworks, we first carried out a detailed description and comparative analysis of related important algorithms, and sorted out the differences, common points and inherent relationships. Considering the wide application of deep learning in UAV remote sensing, we propose some hypotheses on how to introduce deep learning technology into above three orthoimage generation frameworks. After analysis, we provide a more detailed performance quantification comparison of the two most recent potential frameworks (State-of-the-art methods based on SfM and SLAM). It is worth noting that we integrated different test data sources of UVA aerial video sequence with a general SLAM testing platform, and solve the issue that SLAM-based orthoimage generation methods are difficult to evaluate cross-platform. Finally, challenges about visual UAV orthoimage generation and future directions in addressing these challenges are also pointed out.