The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 461–464, 2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 461–464, 2017

  31 May 2017

31 May 2017

Learning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images

C. Chen1, W. Gong1, Y. Hu2, Y. Chen1, and Y. Ding2 C. Chen et al.
  • 1Key Lab of Optoelectronic Technology & Systems of Education Ministry, Chongqing University, Chongqing 400044, China
  • 2Chongqing Geomatics Centre, Chongqing 401121, China

Keywords: VGG, Building detection, Deep learning, OR-CNN, Satellite remote sensing images

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.