Multi-epoch and Multi-Imagery (MEMI) Photogrammetric Workflow for Enhanced Change Detection Using Time-Lapse Cameras
| Autor: | Xabier Blanch, Anette Eltner, Marta Guinau, Antonio Abellán |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer science Science time-lapse photogrammetry multi-view stereo 3D point clouds change detection rockslope monitoring Multi-Epoch and Multi-Imagery (MEMI) 0211 other engineering and technologies Point cloud ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 01 natural sciences Fotogrametria Structure from motion Computer vision 021101 geological & geomatics engineering 0105 earth and related environmental sciences Lectors òptics business.industry 3D reconstruction Automation Workflow Photogrammetry Lidar Vigilància electrònica Optical scanners Electronic surveillance General Earth and Planetary Sciences Artificial intelligence business Change detection |
| Zdroj: | Dipòsit Digital de la UB Universidad de Barcelona Remote Sensing, Vol 13, Iss 1460, p 1460 (2021) Remote Sensing; Volume 13; Issue 8; Pages: 1460 |
| Popis: | Photogrammetric models have become a standard tool for the study of surfaces, structures and natural elements. As an alternative to Light Detection and Ranging (LiDAR), photogrammetry allows 3D point clouds to be obtained at a much lower cost. This paper presents an enhanced workflow for image-based 3D reconstruction of high-resolution models designed to work with fixed time-lapse camera systems, based on multi-epoch multi-images (MEMI) to exploit redundancy. This workflow is part of a fully automatic working setup that includes all steps: from capturing the images to obtaining clusters from change detection. The workflow is capable of obtaining photogrammetric models with a higher quality than the classic Structure from Motion (SfM) time-lapse photogrammetry workflow. The MEMI workflow reduced the error up to a factor of 2 when compared to the previous approach, allowing for M3C2 standard deviation of 1.5 cm. In terms of absolute accuracy, using LiDAR data as a reference, our proposed method is 20% more accurate than models obtained with the classic workflow. The automation of the method as well as the improvement of the quality of the 3D reconstructed models enables accurate 4D photogrammetric analysis in near-real time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|