Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 613-617, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-613-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 613-617, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-613-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 May 2017

31 May 2017

ACCURATE AND FAST BUILDING DETECTION USING BINARY BAG-OF-FEATURES

Y. Hu1,2, Z. Li3, P. Li1, Y. Ding1, and Y. Liu3 Y. Hu et al.
  • 1Chongqing Geomatics Center, 401121 Chongqing, China
  • 2School of Remote Sensing and Information Engineering, Wuhan University, 430079 Wuhan, China
  • 3College of Optoelectronic Engineering, Chongqing University, 400044 Chongqing, China

Keywords: Building Detection, Simple Linear Iterative Clustering, Binary Bag-Of-Features, Machine Learning, Local Feature, Descriptor, Classification

Abstract. This paper presents a non-interactive building detection approach employing binary bag-of-features (BBOF), namely, extracting building roof contours in remote sensing images automatically, rapidly and accurately. The proposed method includes two major stages, i.e., building area detection and building contours extraction. In the first stage, it contains three modules. i.e., oversegmentation, intersection point classification, building area detection. Firstly, the orthophoto is over-segmented by the Simple Linear Iterative Clustering (SLIC) superpixel segmentation method, and the intersection points is obtained. Secondly, the oriented FAST and rotated BRIEF (ORB) descriptors are generated in LAB colour space from the patches that centred on the intersection points, and the BBOF classifier is adopted to classify the intersection points into two categories. Thirdly, the area that contains of the building roof are detected through reserving the regions around the intersection points in inner parts of building roof, and eliminating the regions around the intersection points in non-building roof. At last, we can roughly generate the building area. For the second stage, it is similar to the first one while the main difference is that its classifier has three categories. Finally, we provide an evaluation between two different classifiers, including ORB+BBOF and SURF+BOF. This evaluation is conducted on orthophotos with different roof colours, texture, shape, size and orientation. The proposed approach presents several advantages in terms of scalability, suitability and simplicity with respect to the existing methods.