Hwang, S.; Lee, J.H.; Ha, C.S.; Yang, M.; Choi, J.H. Real-Time 2D Orthomosaic Mapping from Drone-Captured Images Using Feature-Based Sequential Image Registration. Preprints2023, 2023120190. https://doi.org/10.20944/preprints202312.0190.v1
APA Style
Hwang, S., Lee, J.H., Ha, C.S., Yang, M., & Choi, J.H. (2023). Real-Time 2D Orthomosaic Mapping from Drone-Captured Images Using Feature-Based Sequential Image Registration. Preprints. https://doi.org/10.20944/preprints202312.0190.v1
Chicago/Turabian Style
Hwang, S., Minuk Yang and Jae Ho Choi. 2023 "Real-Time 2D Orthomosaic Mapping from Drone-Captured Images Using Feature-Based Sequential Image Registration" Preprints. https://doi.org/10.20944/preprints202312.0190.v1
Abstract
This study presents a method for the rapid and accurate generation of two-dimensional (2D) orthomosaic maps using selected image data collected by drone-captured video. The focus is on developing a real-time method capable of creating maps more quickly than image selection. The scale-invariant feature transform (SIFT) algorithm is applied to drone images to extract features in various scale regions. For feature point matching, a matching method based on the fast library for approximate nearest neighbors (FLANN) was adopted. A comparison of the computational speed of the FLANN with that of the k-nearest neighbors (KNN) and brute force matcher during the matching process revealed FLANN's superior capability for real-time data processing. The random sample consensus (RANSAC) algorithm was employed to enhance the accuracy of the matching by removing outliers, effectively identifying and eliminating mismatches, and reinforcing the reliability of feature point matching. The combination of SIFT, FLANN, and RANSAC algorithms demonstrates the capacity to process drone-captured image data in real time, facilitating the generation of precise 2D orthomosaic maps. The proposed method was assessed and validated using imagery obtained as the drone executed curvilinear and straight flight paths, confirming its accuracy and operational efficiency concurrently with the image capture process.
Keywords
two-dimensional (2D) orthomosaic; drone video processing; real-time image registration; sequential registration; scale-invariant feature transform (SIFT); fast library for approximate nearest neighbors (FLANN); random sample consensus (RANSAC)
Subject
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
