Abstract
Unmanned Aerial Vehicles (UAVs) have gained spotlighted attention in the recent past and has experienced exponential advancements. This research focuses on UAV-based data acquisition and processing to generate highly accurate outputs pertaining to orthomosaic imagery, elevation, surface and terrain models. The study addresses the challenges inherent in the generation and analysis of orthomosaic images, particularly the critical need for correction and enhancement to ensure precise application in fields like detailed mapping and continuous monitoring. To achieve superior image quality and precision, the study applies advanced image processing techniques encompassing Fuzzy Logic and edge-detection techniques. The study emphasizes on the necessity of an approach for countering the loss of information while mapping the UAV deliverables. By offering insights into both the challenges and solutions related to orthomosaic image processing, this research lays the groundwork for future applications that promise to further increase the efficiency and effectiveness of UAV-based methods in geomatics, as well as in broader fields such as engineering and environmental management.
Keywords
UAV; orthomosaic; fuzzy logic; spatial mapping; image enhancement
References
Hodam H, Rienow A, Jürgens C. Bringing Earth Observation to Schools with Digital Integrated Learning Environments. Remote Sensing. 2020; 12(3): 345. doi: 10.3390/rs12030345
Ye X, Zhang L, & Wu Y. Improving UAV orthomosaics by feature matching and multiple flight datasets. Journal of Photogrammetry and Remote Sensing. 2019; 149: 101–115. doi: 10.1016/j.isprsjprs.2019.01.003
Zhu Z. Science of Landsat Analysis Ready Data. Remote Sensing. 2019; 11(18): 2166. doi: 10.3390/rs11182166
Egorov A, Roy D, Zhang H, et al. Landsat 4, 5 and 7 (1982 to 2017) Analysis Ready Data (ARD) Observation Coverage over the Conterminous United States and Implications for Terrestrial Monitoring. Remote Sensing. 2019; 11(4): 447. doi: 10.3390/rs11040447
Nex F. UAV-g 2015 - Unmanned Aerial Vehicles in Geomatics. The Photogrammetric Record. 2015; 30(150): 250–250.
Shakhatreh H, Sawalmeh AH, Al-Fuqaha A, et al. Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges. IEEE Access. 2019; 7: 48572–48634. doi: 10.1109/access.2019.2909530
Restas A. Drone Applications for Supporting Disaster Management. World Journal of Engineering and Technology. 2015; 03(03): 316–321. doi: 10.4236/wjet.2015.33c047
Ye J, Zhang C, Lei H, et al. Secure UAV-to-UAV Systems with Spatially Random UAVs. IEEE Wireless Communications Letters. 2019; 8(2): 564–567. doi: 10.1109/lwc.2018.2879842
Nguyen T, Pham D, & Tran H. Fuzzy logic and edge-detection techniques for remote sensing image enhancement. Remote Sensing. 2021; 13(5): 1129.
Singh A, Sharma P, & Kumar V. Adaptive fuzzy logic approaches for pixel interpolation in UAV orthomosaics. International Journal of Remote Sensing. 2022; 43(10): 2525–2540.
Román A, Heredia S, Windle AE, et al. Enhancing Georeferencing and Mosaicking Techniques over Water Surfaces with High-Resolution Unmanned Aerial Vehicle (UAV) Imagery. Remote Sensing. 2024; 16(2): 290. doi: 10.3390/rs16020290
Román A, Tovar-Sánchez A, Gauci A, et al. Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters. Remote Sensing. 2022; 15(1): 237. doi: 10.3390/rs15010237
Essel B, Bolger M, McDonald J, et al. Developing a theoretical assessment method for an assisted direct georeferencing approach to improve accuracy when mapping over water: the concept, potential and limitations. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2023; XLVIII-1/W1-2023: 139–144. doi: 10.5194/isprs-archives-xlviii-1-w1-2023-139-2023
Seifert E, Seifert S, Vogt H, et al. Influence of Drone Altitude, Image Overlap, and Optical Sensor Resolution on Multi-View Reconstruction of Forest Images. Remote Sensing. 2019; 11(10): 1252. doi: 10.3390/rs11101252
Xiang R, Sun M, Jiang C, et al. A method of fast mosaic for massive UAV images. Jackson TJ, Chen JM, Gong P, Liang S, eds. Land Surface Remote Sensing II. 2014; 9260: 92603W. doi: 10.1117/12.2069201
Cui J hui, Wei R xuan, Liu Z cheng, et al. UAV Motion Strategies in Uncertain Dynamic Environments: A Path Planning Method Based on Q-Learning Strategy. Applied Sciences. 2018; 8(11): 2169. doi: 10.3390/app8112169
Aboutalebi M, Torres-Rua AF, McKee M, et al. Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models. Remote Sensing. 2019; 12(1): 50. doi: 10.3390/rs12010050
Fritz A, Kattenborn T, & Koch B. UAV-based photogrammetric point clouds – tree stem mapping in open stands in comparison to terrestrial laser scanner point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2013; XL-1/W2: 141–146. doi: 10.5194/isprsarchives-XL-1-W2-141-2013
Rhee S, Kim T. Dense 3d point cloud generation from UAV images from image matching and global optimazation. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B1: 1005–1009. doi: 10.5194/isprsarchives-xli-b1-1005-2016
Malihi S, Valadan Zoej MJ, Hahn M. Large-Scale Accurate Reconstruction of Buildings Employing Point Clouds Generated from UAV Imagery. Remote Sensing. 2018; 10(7): 1148. doi: 10.3390/rs10071148
Zhang J, Xu S, Zhao Y, et al. Aerial orthoimage generation for UAV remote sensing: Review. Information Fusion. 2023; 89: 91–120. doi: 10.1016/j.inffus.2022.08.007
Sangjan W, McGee RJ, Sankaran S. Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop. Remote Sensing. 2022; 14(10): 2396. doi: 10.3390/rs14102396
Mayathevar K, Veluchamy M, Subramani B. Fuzzy color histogram equalization with weighted distribution for image enhancement. Optik. 2020; 216: 164927. doi: 10.1016/j.ijleo.2020.164927
Abbasi N, Khan MF, Khan E, et al. Fuzzy histogram equalization of hazy images: a concept using a type-2-guided type-1 fuzzy membership function. Granular Computing. 2022; 8(4): 731–745. doi: 10.1007/s41066-022-00351-0
Ludwig M, M. Runge C, Friess N, et al. Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics. Remote Sensing. 2020; 12(22): 3831. doi: 10.3390/rs12223831
Forlani G, Dall’Asta E, Diotri F, et al. Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning. Remote Sensing. 2018; 10(2): 311. doi: 10.3390/rs10020311
Sammartano G, Spanò A. DEM Generation based on UAV Photogrammetry Data in Critical Areas. Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management. Published online 2016: 92–98. doi: 10.5220/0005918400920098
Escobar Villanueva JR, Iglesias Martínez L, Pérez Montiel JI. DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations. Sensors. 2019; 19(14): 3205. doi: 10.3390/s19143205