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- Title
Learning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images.
- Authors
Chaoyue Chen; Weiguo Gong; Yan Hu; Yongliang Chen; Yi Ding
- Abstract
The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN). Our contributions are mainly in the following two aspects: 1) Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2) the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.
- Subjects
ARTIFICIAL neural networks; REMOTE sensing
- Publication
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2017, Vol 42, Issue 1/W1, p461
- ISSN
1682-1750
- Publication type
Article
- DOI
10.5194/isprs-archives-XLII-1-W1-461-2017