Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal Geoplanning : Journal of Geomatics and Planning
Oluibukun Gbenga Ajayi
Department of Surveying and Geoinformatics, Federal University of Technology, Minna, PMB 65, Minna, Nigeria
Earth & Planetary Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Modelling 3D Topography by Comparing Airborne Lidar Data with Unmanned Aerial System (UAS) Photogrammetry Under Multiple Imaging Conditions Oluibukun Gbenga Ajayi; Mark Palmer
Geoplanning: Journal of Geomatics and Planning Vol 6, No 2 (2019)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.6.2.122-138

Abstract

This study presents the effect of image data sources on the topographic modelling of part of the National Trust site located at Weston-Super-Mare, Bristol, United Kingdom, covering an approximate area of 1.82 hectares. The accuracy of the DEM generated from 1m resolution and 2m resolution LiDAR data together with the accuracy of the DEM generated from the UAV images acquired at different altitudes are analysed using the 1 m LiDAR DEM as reference for the accuracy assessment. Using the NSSDA methodology, the horizontal and vertical accuracy of the DEMs generated from each of the four sources were computed while the paired sample t-test was conducted to ascertain the existence of statistically significant difference between the means of the X, Y, and Z coordinates of the check points. The result obtained shows that with a RMSE of -0.0101499 and horizontal accuracy of -0.175674686m, the planimetric coordinates extracted from 2 m LiDAR DEM were more accurate than the planimetric coordinates extracted from the UAV based DEMs while the UAV based DEMs proved to be more accurate than the 2m LiDAR DEM in terms of altimetric coordinates, though the DEM generated from UAV images acquired at 50 m altitude gave the most accurate result when compared with the vertical accuracy obtained from the DEM generated from UAV images acquired at 30 m and 70 m flight heights. These findings are also consistent with the result of the statistical analysis at 95% confidence interval.
PRELIMINARY INVESTIGATION OF THE ROBUSTNESS OF MAXIMALLY STABLE EXTREMAL REGIONS (MSER) MODEL FOR THE AUTOMATIC REGISTRATION OF OVERLAPPING IMAGES Oluibukun Gbenga Ajayi; Ifeanyi Jonathan Nwadialor; Ifeanyi Chukwudi Onuigbo; Olurotimi Adebowale Kemiki
Geoplanning: Journal of Geomatics and Planning Vol 5, No 1 (2018)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2151.998 KB) | DOI: 10.14710/geoplanning.5.1.63-74

Abstract

Various researchers in Digital Image processing have developed keen interest in the automation of object detection, description and extraction process used for various applications and this has led to the development of series of Feature detection and extraction models one of which is the Maximally Stable Extremal Regions Feature Algorithm (MSER).  This paper investigates the robustness of MSER algorithm (a blob-like and affine-invariant feature detector) for the detection and extraction of corresponding features used for the automatic registration of series of overlapping images. The robustness investigation was carried out in three different registration campaigns using overlapping images extracted from google earth and image pair acquired from an Unmanned Aerial Vehicle (UAV). Sum of Square Difference (SSD) and Bilinear interpolation models were used to establish the similarity measure between the images to be registered, resampling of the pixel-values and computation of non-integer coordinates respectively while Random Sampling Consensus (RANSAC) algorithm was used to exclude the outliers and to compute the transformation matrix using affine transformation function. The results obtained from this preliminary investigation shows that the processing speed of MSER is quite high for Automatic Image Registration with a relatively high accuracy. While an accuracy of 61.54% was obtained from the first campaign with a processing time of 11.92 seconds, the second campaign gave an accuracy of 52.02% with a processing time of 11.20 seconds and the third campaign produced an accuracy of 55.62% with a processing time of 3.27 seconds. The obtained speed and accuracy shows that MSER is a very robust model and as such, can be deployed as the feature detection and extraction model in the development of an automatic image registration scheme.
Co-Authors Ifeanyi Chukwudi Onuigbo Ifeanyi Jonathan Nwadialor Mark Palmer Olurotimi Adebowale Kemiki