Building extraction and change detection from remotely sensed imagery based on layered architecture

  • Wenzao ShiEmail author
  • Zhengyuan MaoEmail author
  • Jinqing Liu
Original Article


The processing and analysis of remotely sensed imagery (RSI) is a research hotspot in the information field, and building extraction and change detection are some of the difficult problems. In order to make the maximum use of the effective characteristics and design independently the algorithm of feature extraction, an approach to building extraction and change detection from RSI based on layered architecture containing pixel layer, object layer and configuration layer is proposed. In the pixel layer, the input image is over-segmented and under-segmented, respectively, by a quantity-controllable algorithm using super-pixel segmentation to obtain the segmentation object sets, with which the input image is decomposed into shadow layer, homogeneity layer and edge layer, where the buildings are extracted based on the spatial relationship between the feature areas and segmentation objects. In the object layer, for preserving the accurate contour of the buildings, a new segmentation method based on the traditional graph-cut theory and mathematical morphology is introduced, and then, the buildings extracted from each layer are merged. Finally, in the configuration layer, the change information is detected using spatial relationship of buildings between the old image and the new one. The experimental results reveal that the building contour is extracted accurately, and three types of change including the newly built, the demolished and the reconstructed buildings can be detected; in addition, there is no strict requirement for registration accuracy. For the test images, the overall performance F1 of the building extraction is over 85, and the precision and recall of the change detection are both higher than 90%.


Layered architecture Super-pixel segmentation Shadow Homogeneity Edge Remotely sensed imagery Building extraction Change detection 



This work was supported by National Natural Science Foundation of China (Grant No. 41701491), Natural Science Foundation of Fujian Province, China (Grant No. 2017J01464, 2018J01619), Special Funds of the Central Government Guiding Local Science and Technology Development (Grant No.2017L3009) and Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT_15R10).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal UniversityFuzhouPeople’s Republic of China
  2. 2.Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics TechnologyFujian Normal UniversityFuzhouPeople’s Republic of China
  3. 3.Key Lab of Spatial Data Mining and Information Sharing of Ministry of EducationFuzhou UniversityFuzhouPeople’s Republic of China
  4. 4.National Engineering Research Centre of Geospatial Information TechnologyFuzhou UniversityFuzhouPeople’s Republic of China
  5. 5.Spatial Information Engineering Research Centre of Fujian ProvinceFuzhou UniversityFuzhouPeople’s Republic of China

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