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

Remote Sensing

Xiaomin Qiu¹  Chunyan Li¹  Nan Shang¹  Eddie Weeks²  Kaiguang Zhao³  Xukai Zhang³  Xuelian Meng³ 

[1] Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA;Coastal Studies Institute, Louisiana State University, Baton Rouge, LA 70803, USA;;Department of Geography &

关键词: photogrammetric UAV;    high resolution;    coastal topographic mapping;    wetland restoration;    classification correction;    terrain correction;    object-oriented analysis;    classification ensemble;   

DOI  :  10.3390/rs9111187

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown