Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

Version 1 : Received: 17 May 2021 / Approved: 18 May 2021 / Online: 18 May 2021 (10:10:07 CEST)

A peer-reviewed article of this Preprint also exists.

Lim, P.-C.; Rhee, S.; Seo, J.; Kim, J.-I.; Chi, J.; Lee, S.-B.; Kim, T. An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images. Remote Sens. 2021, 13, 2118. Lim, P.-C.; Rhee, S.; Seo, J.; Kim, J.-I.; Chi, J.; Lee, S.-B.; Kim, T. An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images. Remote Sens. 2021, 13, 2118.

Journal reference: Remote Sens. 2021, 13, 2118
DOI: 10.3390/rs13112118

Abstract

Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips, and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW), and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.

Subject Areas

UAV Images; Monoscopic Mapping; Stereoscopic Plotting; Image Overlap; Optimal Image Selection

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.