Abstract
Post-earthquake high resolution (HR) remote sensing image classification is crucial for disaster assessment and emergency rescue. 3-D convolutional neural networks (3-D CNNs) exhibit promising performance in remote sensing image classification. However, 3-D CNNs lack the theoretical underpinnings to perform multiresolution approximation for filter learning in view of the scale variance of natural objects. Gabor filtering can effectively extract multiresolution spatial information including edges and textures, which have a potential to reinforce the robustness of learned features in 3-D CNNs against the orientation and scale changes. In this paper, we propose a combined 3-D convolutional neural network and Gabor filters (GNN) method for post-earthquake HR image classification. Instead of choosing a single scale, GNN extends the spatial information to several scales by Gabor filters to take advantage of correlations among multiple scales for damage mapping. The experimental results show that GNN can reflect the multiscale information of complex scenes, obtain good classification results for mapping post-earthquake damage using HR remote sensing images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, Y., Wu, L.: Geological disaster recognition on optical remote sensing images using deep learning. Proc. Comput. Sci. 91, 566–575 (2016). https://doi.org/10.1016/j.procs.2016.07.144
Gencer, E.A.: Natural disasters, urban vulnerability, and risk management: a theoretical overview. In: Gencer, E.A. (ed.) The Interplay between Urban Development, Vulnerability, and Risk Management: A Case Study of the Istanbul Metropolitan Area, pp. 7–43. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-29470-9_2
Geiß, C., Aravena Pelizari, P., Marconcini, M., et al.: Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques. ISPRS J. Photogramm. Remote Sens. 104, 175–188 (2015). https://doi.org/10.1016/j.isprsjprs.2014.07.016
Cooner, A., Shao, Y., Campbell, J.: Detection of urban damage using remote sensing and machine learning algorithms: revisiting the 2010 Haiti earthquake. Remote Sens. 8(10), 868 (2016). https://doi.org/10.3390/rs8100868
Béjarpizarro, M., Notti, D., Mateos, R.M., et al.: Mapping vulnerable urban areas affected by slow-moving landslides using sentinel-1 InSAR data. Remote Sens. 9(9), 876 (2017). https://doi.org/10.3390/rs9090876
Yan, Y., Ren, J., Sun, G., et al.: Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement. Pattern Recognit. 79, 65–78 (2018). https://doi.org/10.1016/j.patcog.2018.02.004
Cheng, G., Han, J., Guo, L., et al.: Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE Trans. Geosci. Remote Sens. 53(8), 4238–4249 (2015). https://doi.org/10.1109/TGRS.2015.2393857
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539
Zhao, W., Du, S.: Learning multiscale and deep representations for classifying remotely sensed imagery. ISPRS J. Photogramm. Remote Sens. 113, 155–165 (2016). https://doi.org/10.1016/j.isprsjprs.2016.01.004
Yu, S., Jia, S., Xu, C.: Convolutional neural networks for hyperspectral image classification. Neurocomputing 219, 88–98 (2016). https://doi.org/10.1016/j.neucom.2016.09.010
Bouvrie, J.: Notes on Convolutional Neural Networks. Neural Nets (2006)
Liu, F., Lin, G., Shen, C.: CRF learning with CNN features for image segmentation. Pattern Recogn. 48(10), 2983–2992 (2015). https://doi.org/10.1016/j.patcog.2015.04.019
Sun, G., Ma, P., Ren, J., et al.: A stability constrained adaptive alpha for gravitational search algorithm. Knowl. Based Syst. 139, 200–213 (2018). https://doi.org/10.1016/j.knosys.2017.10.018
Zhang, A., Sun, G., Ren, J., et al.: A dynamic neighborhood learning-based gravitational search algorithm. IEEE Trans. Cybern. 48(1), 436–447 (2018). https://doi.org/10.1109/TCYB.2016.2641986
Sun, G., Hao, Y., Rong, J., et al.: Combined deep learning and multiscale segmentation for rapid high resolution damage mapping. In: IEEE International Conferences (CPSCom, GreenCom, iThings, SmartData), pp. 1101–1105 (2017). https://doi.org/10.1109/ithings-greencom-cpscom-smartdata.2017.238
Shi, C., Pun, C.-M.: 3D multi-resolution wavelet convolutional neural networks for hyperspectral image classification. Inf. Sci. 420, 49–65 (2017). https://doi.org/10.1016/j.ins.2017.08.051
Luan, S., Chen, C., Zhang, B., et al.: Gabor convolutional networks. IEEE Trans. Image Process. (2018). https://doi.org/10.1109/tip.2018.2835143
Li, J., Wang, T., Zhou, Y., et al.: Using Gabor filter in 3D convolutional neural networks for human action recognition. In: 2017 36th Chinese Control Conference (CCC), 26–28 July 2017, pp. 11139–11144 (2017)
Bhagavathy, S., Manjunath, B.S.: Modeling and detection of geospatial objects using texture motifs. IEEE Trans. Geosci. Remote Sens. 44(12), 3706–3715 (2006). https://doi.org/10.1109/TGRS.2006.881741
Yuan, J., Wang, D., Li, R.: Remote sensing image segmentation by combining spectral and texture features. IEEE Trans. Geosci. Remote Sens. 52(1), 16–24 (2014). https://doi.org/10.1109/TGRS.2012.2234755
Gu, J., Wang, Z., Kuen, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. (2017). https://doi.org/10.1016/j.patcog.2017.10.013
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/bf00994018
Benz, U.C., Hofmann, P., Willhauck, G., et al.: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 58(3–4), 239–258 (2004). https://doi.org/10.1016/j.isprsjprs.2003.10.002
Han, J., Zhang, D., Cheng, G., Guo, L., Ren, J.: Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans. Geosci. Remote Sens. 53(6), 3325–3337 (2015). https://doi.org/10.1109/TGRS.2014.2374218
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Hao, Y. et al. (2018). 3-D Gabor Convolutional Neural Network for Damage Mapping from Post-earthquake High Resolution Images. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-00563-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00562-7
Online ISBN: 978-3-030-00563-4
eBook Packages: Computer ScienceComputer Science (R0)