Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Building Change Detection Using Deep Learning for Remote Sensing Images

  • Wang, Chang (State Key Laboratory of Geo-Information Engineering) ;
  • Han, Shijing (School of Natural Resources and Surveying, Nanning Normal University) ;
  • Zhang, Wen (School of Civil Engineering, University of Science and Technology Liaoning) ;
  • Miao, Shufeng (Wuhan Kedao Geographical Information Engineering Co., Ltd.)
  • Received : 2022.01.04
  • Accepted : 2022.05.23
  • Published : 2022.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.

Keywords

References

  1. X. Huang, L. Zhang, and T. Zhu, "Building change detection from multitemporal high-resolution remotely sensed images based on a morphological building index," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 1, pp. 105-115, 2014. https://doi.org/10.1109/JSTARS.2013.2252423
  2. T. Feng and Z. F. Shao, "Building change detection based on the enhanced morphological building index," Science of Surveying and Mapping, vol. 2017, no. 5, pp. 29-34, 2017.
  3. X. Ye, Q. Qi, J. Wang, X. Zheng, and J. Wang, "Detecting damaged buildings caused by earthquake from remote sensing image using local spatial statistics method," Geomatics and Information Science of Wuhan University, vol. 44, no. 1, pp. 125-131, 2019.
  4. J. Li, J. Dang, and Y. Wang, "Building change detection by multi-feature fusion from high resolution remote sensing images," Bulletin of Surveying and Mapping, vol. 2019, no. 10, pp. 105-108, 2019.
  5. X. Huang, T. Zhu, L. Zhang, and Y. Tang, "A novel building change index for automatic building change detection from high-resolution remote sensing imagery," Remote Sensing Letters, vol. 5, no. 8, pp. 713-722, 2014. https://doi.org/10.1080/2150704X.2014.963732
  6. Z. Zhang, X. Zhang, Q. Xin, and X. Yang, "Combining the pixel-based and object-based methods for building change detection using high-resolution remote sensing images," Acta Geodaetica et Cartographica Sinica, vol. 47, no. 1, pp. 102-112, 2018.
  7. K. Nemoto, R. Hamaguchi, M. Sato, A. Fujita, T. Imaizumi, and S. Hikosaka, "Building change detection via a combination of CNNs using only RGB aerial imageries," in Proceedings of SPIE 10431: Remote Sensing Technologies and Applications in Urban Environments II. Bellingham, WA: International Society for Optics and Photonics, 2017, pp. 107-118.
  8. A. M. El Amin, Q. Liu, and Y. Wang, "Zoom out CNNs features for optical remote sensing change detection," in Proceedings of 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, China, 2017, pp. 812-817.
  9. D. Liu, L. Han, and X. Han, "High spatial resolution remote sensing image classification based on deep learning," Acta Optica Sinica, vol. 36, no. 4, pp. 306-314, 2016.
  10. W. Zhao and S. Du, "Learning multiscale and deep representations for classifying remotely sensed imagery," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 113, pp. 155-165, 2016. https://doi.org/10.1016/j.isprsjprs.2016.01.004
  11. Y. Liu, Z. Zhang, R. Zhong, D. Chen, Y. Ke, J. Peethambaran, C. Chen, and L. Sun, "Multilevel building detection framework in remote sensing images based on convolutional neural networks," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 10, pp. 3688-3700, 2018. https://doi.org/10.1109/jstars.2018.2866284
  12. M. Vakalopoulou, K. Karantzalos, N. Komodakis, and N. Paragios, "Building detection in very high resolution multispectral data with deep learning features," in Proceedings of 2015 IEEE international Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 2015, pp. 1873-1876.
  13. Q. Wang, X. Zhang, G. Chen, F. Dai, Y. Gong, and K. Zhu, "Change detection based on Faster R-CNN for high-resolution remote sensing images," Remote Sensing Letters, vol. 9, no. 10, pp. 923-932, 2018. https://doi.org/10.1080/2150704x.2018.1492172
  14. C. Wang, Y. Zhang, and S. Han, "Remote sensing image change detection based on frequency domain significance method and ELM," Journal of Huazhong University of Science and Technology (Natural Science Edition), vol. 48, no. 5, pp. 19-24, 2020.
  15. C. Wang, Y. Zhang, S. Ji, and L. Zhang, "Multi-feature fusion and random multi-graph synthetic building change method," Acta Geodaetica et Cartographica Sinica, vol. 50, no. 2, pp. 235-247, 2021.
  16. C. Wang and X. Wang, "Building change detection from multi-source remote sensing images based on multifeature fusion and extreme learning machine," International Journal of Remote Sensing, vol. 42, no. 6, pp. 2246-2257, 2021. https://doi.org/10.1080/2150704X.2020.1805134
  17. C. Wang, Y. S. Zhang, X. Wang, and Y. Yu, "Remote sensing image change detection method based on deep neural networks," Journal of ZheJiang University (Engineering Science), vol. 54, no. 11, pp. 2138-2148, 2020.
  18. X. Zhang, X. Chen, F. Li, and T. Yang, "Change detection method for high resolution remote sensing images using deep Learning," Acta Geodaetica et Cartographica Sinica, vol. 46, no. 8, pp. 999-1008, 2017.