Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

• Published in: Remote sensing (Basel, Switzerland) Vol. 9; no. 11; p. 1187
• Main Authors: Meng, Xuelian, Shang, Nan, Zhang, Xukai, Li, Chunyan, Zhao, Kaiguang, Qiu, Xiaomin, Weeks, Eddie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2017
• Subjects: Aerial surveys; Algorithms; Classification; classification correction; classification ensemble; coastal topographic mapping; Environmental restoration; Error correction; high resolution; Learning algorithms; Machine learning; Mapping; Object oriented programming; object-oriented analysis; photogrammetric UAV; Photogrammetry; Problem solving; Restoration; terrain correction; Terrain mapping; Vegetation; Vegetation cover; wetland restoration
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs9111187

Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by ~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments.