A Hierarchical Object-oriented Urban Land Cover Classification Using WorldView-2 Imagery and Airborne LiDAR data

6th Digital Earth Summit

地球科学;计算机科学

Wu, M.F.^1,2 ; Sun, Z.C.^2,3 ; Yang, B.^1 ; Yu, S.S.^2,4

Hunan Normal University, Changsha, Hunan

410006, China^1

Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing

100094, China^2

Hainan Key Laboratory Earth Observation, Sanya, Hainan

572029, China^3

Shandong University of Science and Technology, Qingdao, Shandong

266510, China^4

关键词: Digital surface models;    Hierarchical classification;    Land cover;    Light detection and ranging;    Object oriented;    Pixel based classifications;    Urban land cover classification;    Worldview-2;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/46/1/012016/pdf
DOI  :  10.1088/1755-1315/46/1/012016

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

In order to reduce the "salt and pepper" in pixel-based urban land cover classification and expand the application of fusion of multi-source data in the field of urban remote sensing, WorldView-2 imagery and airborne Light Detection and Ranging (LiDAR) data were used to improve the classification of urban land cover. An approach of object- oriented hierarchical classification was proposed in our study. The processing of proposed method consisted of two hierarchies. (1) In the first hierarchy, LiDAR Normalized Digital Surface Model (nDSM) image was segmented to objects. The NDVI, Costal Blue and nDSM thresholds were set for extracting building objects. (2) In the second hierarchy, after removing building objects, WorldView-2 fused imagery was obtained by Haze-ratio-based (HR) fusion, and was segmented. A SVM classifier was applied to generate road/parking lot, vegetation and bare soil objects. (3) Trees and grasslands were split based on an nDSM threshold (2.4 meter). The results showed that compared with pixel-based and non-hierarchical object-oriented approach, proposed method provided a better performance of urban land cover classification, the overall accuracy (OA) and overall kappa (OK) improved up to 92.75% and 0.90. Furthermore, proposed method reduced "salt and pepper" in pixel-based classification, improved the extraction accuracy of buildings based on LiDAR nDSM image segmentation, and reduced the confusion between trees and grasslands through setting nDSM threshold.