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http://dx.doi.org/10.5909/JBE.2021.26.1.14

Change Attention based Dense Siamese Network for Remote Sensing Change Detection  

Hwang, Gisu (Department of Electronic Engineering, Kwangwoon University)
Lee, Woo-Ju (Kwangwoon University)
Oh, Seoung-Jun (Department of Electronic Engineering, Kwangwoon University)
Publication Information
Journal of Broadcast Engineering / v.26, no.1, 2021 , pp. 14-25 More about this Journal
Abstract
Change detection, which finds changes in remote sensing images of the same location captured at different times, is very important because it is used in various applications. However, registration errors, building displacement errors, and shadow errors cause false positives. To solve these problems, we propose a novle deep convolutional network called CADNet (Change Attention Dense Siamese Network). CADNet uses FPN (Feature Pyramid Network) to detect multi-scale changes, applies a Change Attention Module that attends to the changes, and uses DenseNet as a feature extractor to use feature maps that contain both low-level and high-level features for change detection. CADNet performance measured from the Precision, Recall, F1 side is 98.44%, 98.47%, 98.46% for WHU datasets and 90.72%, 91.89%, 91.30% for LEVIR-CD datasets. The results of this experiment show that CADNet can offer better performance than any other traditional change detection method.
Keywords
Change Detection; Remote Sensing Image; Siamese Network; Attention Mechanism; Densely Connected Convolutional Network;
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1 A. Asokan, and J. Anitha, "Change detection techniques for remote sensing applications: a survey." Earth Science Informatics, Vol.12, No.2, pp.143-160, March 2019.   DOI
2 J. Liu, M. Gong, K. Qin, and P. Zhang, "A deep convolutional coupling network for change detection based on heterogeneous optical and radar images." IEEE transactions on neural networks and learning systems, Vol.29, No.3, pp.545-559, March 2016.   DOI
3 Y. Liu, Q. Ren, J. Geng, M. Ding, and J. Li, "Efficient patch-wise se- mantic segmentation for large-scale remote sensing images." Sensors, Vol.18, No.10, pp.1-16, September 2018.   DOI
4 CF. Chen, NT. Son, NB. Chang, CR. Chen, LY. Chang, M. Valdez, G. Centeno, C. A. Thompson, and J. L. Aceituno, "Multi-decadal mangrove forest change detection and prediction in Honduras, Central America, with Landsat imagery and a Markov chain model." Remote Sensing, Vol.5, No.12, pp.6408-6426, November 2013.   DOI
5 R. C. Daudt, B. Le Saux, A. Boulch, and Y. Gousseau, "Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks," Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, pp.2115-2118, 2018.
6 B. Feizizadeh, T. Blaschke, D. Tiede, and M. H.R. Moghaddam, "Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes." Geomorphology, Vol.293, pp.240-254, September 2017.   DOI
7 J. Im, J.R. Jensen, and J.A. Tullis, "Object‐based change detection using correlation image analysis and image segmentation." International Journal of Remote Sensing, Vol.29, No.2, pp.399-423, April 2008.   DOI
8 T. Blaschke, G. J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R. Q. Feitosa, F. V. D. Werff, F. V. Coillie, and D. Tiede, "Geographic object-based image analysis-towards a new paradigm." ISPRS journal of photogrammetry and remote sensing, Vol.87, pp.180-191, January 2014.   DOI
9 M. Hussain, D. Chen, A. Cheng, H. Wei, and D. Stanley, "Change detection from remotely sensed images: From pixel-based to object-based approaches." ISPRS Journal of photogrammetry and remote sensing, Vol.80, pp.91-106, June 2013.   DOI
10 S. J. Jung, T. H. Kim, W. H. Lee, and Y. K. Han, "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, Vol.37, No.6, pp.481-489, December 2019.   DOI
11 A. R. Song, J. W. Choi, and Y. I. Kim, "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, Vol.37, No.3, pp.199-208, June 2019.   DOI
12 M. E. A. Larabi, S. Chaib, K. Bakhtj, and M. S. Karoui, "Transfer Learning for Changes Detection in Optical Remote Sensing Imagery." Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp.1582-1585, 2019.
13 Y. Liu, C. Pang, Z. Zhan, X. Zhang, and X. Yang, "Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model." arXiv preprint arXiv:1909.07726, 2019.
14 R. C. Daudt, R. L. Saux, and A. Boulch, "Fully convolutional siamese networks for change detection." Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, pp. 4063-4067, 2018.
15 O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation." Proceedings of the International Conference on Medical image computing and computer-assisted intervention, pp.234-241, 2015.
16 Y. Zhang, Y. Zhu, H. Li, S. Chen, J. Peng, and L. Zhao, "Automatic Changes Detection between Outdated Building Maps and New VHR Images Based on Pre-Trained Fully Convolutional Feature Maps." Sensors, Vol.20, No.19, pp.1-20, September 2020.   DOI
17 L. Lan, D. Wu, and M. Chi, "Multi-temporal Change Detection based on Deep Semantic Segmentation Networks," Proceedings of the 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Shanghai, China, pp. 1-4, 2019.
18 K. Simonyan, and A. Zisserman, "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556, 2014.
19 K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada, USA, pp.770-778, 2016.
20 H. Jiang, X. Hu, K. Li, J. Zhang, J. Gong, and M. Zhang, "PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection." Remote Sensing, Vol.12, No.3, pp.1-21, February 2020.
21 H. Chen, and S. Zhenwei, "A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection." Remote Sensing, Vol.12, No.10, pp.1-23, May 2020.
22 X. Lu, W. Wang, C. Ma, J. Shen, L. Shao, and F. Porikli, "See more, know more: Unsupervised video object segmentation with co-attention siamese networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, USA, pp.3623-3632, 2019.
23 G. Huang, Z. Liu, L. V. D. Maaten, and K. Q. Weinberger, "Densely connected convolutional networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, USA, pp.4700-4708, 2017.
24 Wang, Feng, and David M.J. Tax. "Survey on the attention based RNN model and its applications in computer vision." arXiv preprint arXiv:1601.06823, 2016.
25 J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, USA, pp.7132-7141, 2018.
26 S. H. Woo, J. C. Park, J. Y. Lee, and I. S. Kwon, "Cbam: Convolutional block attention module." Proceedings of the European conference on computer vision (ECCV), Munich, Germany, pp.3-19, 2018.
27 X. Wang, R. Girshick, A. Gupta, and K. He, "Non-local neural networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, USA, pp.7794-7803, 2018.
28 S. Ji, S. Wei, and M. Lu, "Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set." IEEE Transactions on Geoscience and Remote Sensing, Vol.57, No.1, pp.574-586, 2018.   DOI
29 Y. Zhan, K. Fu, M. Yan, X. Sun, H. Wang, and X. Qiu, "Change detection based on deep siamese convolutional network for optical aerial images." IEEE Geoscience and Remote Sensing Letters, Vol.14, No.10, pp.1845-1849, October 2017.   DOI
30 S. Brahimi, N. B. Aoun, A. Benoit, P. Lambert, and C. B. Amar, "Semantic segmentation using reinforced fully convolutional densenet with multiscale kernel." Multimedia Tools and Applications, Vol.78, No.15, pp.22077-22098, 2019.   DOI
31 J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, and H. Lu, "Dual attention network for scene segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California, USA, pp.3146-3154, 2019.
32 T. Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, USA, pp.2117-2125, 2017.
33 H. Wang, Z. Wang, M. Du, F. Yang, Z. Zhang, S. Ding, P. Mardziel, and X. Hu, "Score-CAM: Score-weighted visual explanations for convolutional neural networks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp.24-25, 2020.