Analysis of oblique aerial images for land cover and point cloud classification in an Urban environment

Jiann Yeou Rau, Jyun Ping Jhan, Ya Ching Hsu

研究成果: Article同行評審

69 引文 斯高帕斯(Scopus)


In addition to aerial imagery, point clouds are important remote sensing data in urban environment studies. It is essential to extract semantic information from both images and point clouds for such purposes; thus, this study aims to automatically classify 3-D point clouds generated using oblique aerial imagery (OAI)/vertical aerial imagery (VAI) into various urban object classes, such as roof, facade, road, tree, and grass. A multicamera airborne imaging system that can simultaneously acquire VAI and OAI is suggested. The acquired small-format images contain only three RGB spectral bands and are used to generate photogrammetric point clouds through a multiview-stereo dense matching technique. To assign each 3-D point cloud to a corresponding urban object class, we first analyzed the original OAI through object-based image analyses. A rule-based hierarchical semantic classification scheme that utilizes spectral information and geometry-and topology-related features was developed, in which the object height and gradient features were derived from the photogrammetric point clouds to assist in the detection of elevated objects, particularly for the roof and facade. Finally, the photogrammetric point clouds were classified into the aforementioned five classes. The classification accuracy was assessed on the image space, and four experimental results showed that the overall accuracy is between 82.47% and 91.8%. In addition, visual and consistency analyses were performed to demonstrate the proposed classification scheme's feasibility, transferability, and reliability, particularly for distinguishing elevated objects from OAI, which has a severe occlusion effect, image-scale variation, and ambiguous spectral characteristics.

頁(從 - 到)1304-1319
期刊IEEE Transactions on Geoscience and Remote Sensing
出版狀態Published - 2015 3月 1

All Science Journal Classification (ASJC) codes

  • 電氣與電子工程
  • 地球與行星科學(全部)


深入研究「Analysis of oblique aerial images for land cover and point cloud classification in an Urban environment」主題。共同形成了獨特的指紋。