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http://dx.doi.org/10.7780/kjrs.2020.36.6.2.8

Urban Change Detection for High-resolution Satellite Images Using U-Net Based on SPADE  

Song, Changwoo (CONTEC Co., Ltd)
Wahyu, Wiratama (CONTEC Co., Ltd)
Jung, Jihun (CONTEC Co., Ltd)
Hong, Seongjae (CONTEC Co., Ltd)
Kim, Daehee (CONTEC Co., Ltd)
Kang, Joohyung (CONTEC Co., Ltd)
Publication Information
Korean Journal of Remote Sensing / v.36, no.6_2, 2020 , pp. 1579-1590 More about this Journal
Abstract
In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.
Keywords
Change detection; deep learning; satellite images; image segmentation; U-Net;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 Long, J., E. Shelhamer, and T. Darrell, 2015. Fully convolutional networks for semantic segmentation, Proc. of the IEEE conference on computer vision and pattern recognition, Boston, MA, Jun. 8-12, pp. 3431-3440
2 Nielsen, A. A., 2007. The regularized iteratively reweighted MAD method for change detection in multi-and hyperspectral data, IEEE Transactions on Image Processing, 16(2): 463-478.   DOI
3 Otsu, N., 1979. A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66.   DOI
4 Park, T., M.-Y Liu, T.-C Wang, and J.-Y Zhu, 2019. Semantic image synthesis with spatially-adaptive normalization, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, Jun. 16-20, pp. 2337-2346.
5 Ronneberger, O., P. Fischer, and T. Brox, 2015. U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, Springer, Cham, Munich, Germany, pp. 234-241.
6 Song, A.-R, J.-W Choi, and Y.-I Kim, 2019. Change Detection for High-resolution Satellite Images Using Transfer Learning and Deep Learning Network, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 37(3): 199-208 (in Korean with English abstract).   DOI
7 Szeliski, R., 2006. Image alignment and stitching: A tutorial, Foundations and Trends® in Computer Graphics and Vision, 2(1): 1-104.
8 Szegedy, C., V. Vanhoucke, S. loffe, J. Shlens, and Z. Wonjna, 2016. Rethinking the inception architecture for computer vision, Proc. of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, Jun. 26-Jul. 1, pp. 2818-2826.
9 Badrinarayanan, V., A. Kendall, and R. Cipolla, 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12): 2481-2495.   DOI
10 Cicek, O., A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, 2016. 3D U-Net: learning dense volumetric segmentation from sparse annotation, International conference on medical image computing and computer-assisted intervention, Springer, Cham, Munich, Germany, pp. 424-432.
11 Cleve, C., M. Kelly, F. R. Kearns, and M. Moritz, 2008. Classification of the wildland-urban interface: A comparison of pixel-and object-based classifications using high-resolution aerial photography, Computers, Environment and Urban Systems, 32(4): 317-326.   DOI
12 Cortes, C. and V. Vapnik, 1995. Support-vector networks, Machine Learning, 20(3): 273-297.   DOI
13 Wahyu, W., J.-S Lee, and D.-G Sim, 2020. Change Detection on Multi-Spectral Images Based on Feature-level U-Net, IEEE Access, 8: 12279-12289.   DOI
14 Zhou, Z., M. M. R. Siddiquee, and N. Tajbakhsh, 2018. Unet++: A nested u-net architecture for medical image segmentation, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, Cham, Granada, Spain, pp. 3-11.
15 Jung, S.-J., T.-H Kim, W.-H Lee, and Y.-K Han, 2019. Object-based Change Detection using Various Pixel-based Change Detection Results and Registration Noise, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 37(6): 481-489 (in Korean with English abstract).   DOI
16 Bezdek, J.C., R. Ehrlich, and W. Full, 1984. FCM: The fuzzy c-means clustering algorithm, Computers & Geosciences, 10(2-3): 191-203.   DOI
17 Gong, M., H. Yang, and P. Zhang, 2017. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images, ISPRS Journal of Photogrammetry and Remote Sensing, 129: 212-225.   DOI
18 Im, J.-H., J. R. Jensen, and J. A. Tullis, 2008. Objectbased change detection using correlation image analysis and image segmentation, International Journal of Remote Sensing, 29(2): 399-423.   DOI
19 Ioffe, S. and C. Szegedy, 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proc. of 2015 International Conference on Machine Learning, Lille, FRA, Jul. 6-11, pp. 448-456.
20 Jiang, H., X. Hu, K. Li, J. Zhang, J. Gong, and M. Zhang, 2020. PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection, Remote Sensing, 12(3): 484.   DOI