A Deep Learning Framework for Polarimetric SAR Change Detection Using Capsule Network
The Earth's surface is constantly changing through forces of nature and human activities. Reliable, accurate, and timely change detection is critical to environmental monitoring, resource management, and planning activities. Recently, interest in deep learning algorithms, especially convolutional neural networks, has increased in the field of image change detection due to their powerful ability to extract multi-level image features automatically. However, these networks are prone to drawbacks that limit their applications, which reside in their inability to capture spatial relationships between image instances, as this necessitates a large amount of training data. As an alternative, Capsule Network has been proposed to overcome these shortcomings. Although its effectiveness in remote sensing image analysis has been experimentally verified, its application in change detection tasks remains very sparse. Motivated by its greater robustness towards improved hierarchical object representation, this study aims to apply a capsule network for PolSAR image Change Detection. The experimental results demonstrate that the proposed change detection method can yield a significantly higher detection rate compared to methods based on convolutional neural networks.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 355
 M. K. Ridd and J. Liu, “A comparison of four algorithms for change detection in an urban environment,” Remote Sens. Environ., vol. 63, no. 2, pp. 95–100, 1998.
 B. Brisco, A. Schmitt, K. Murnaghan, S. Kaya, and A. Roth, “SAR polarimetric change detection for flooded vegetation,” Int. J. Digit. Earth, vol. 6, no. 2, pp. 103–114, 2013.
 T. Hame, I. Heiler, and J. S. Miguel-Ayanz, “An unsupervised change detection and recognition system for forestry,” Int. J. Remote Sens., vol. 19, no. 6, pp. 1079–1099, 1998.
 G. Liu, L. Jiao, F. Liu, H. Zhong, and S. Wang, “A new patch-based change detector for polarimetric SAR data,” Pattern Recognit., vol. 48, no. 3, pp. 685–695, 2015.
 A.Ghosh, N. S. Mishra, and S. Ghosh, “Fuzzy clustering algorithms for unsupervised change detection in remote sensing images,” Inf. Sci., vol. 181, no. 4, pp. 699–715, 2011.
 M. Liu, H. Zhang, C. Wang, and F. Wu, “Change detection of multilook polarimetric SAR images using heterogeneous clutter models,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 12, pp. 7483–7494, Dec. 2014
 C. Ding and D. Tao, “Robust face recognition via multimodal deep face representation,” IEEE Trans. Multimedia, vol. 17, no. 11, pp. 2049–2058, Nov. 2015.
 J. Li, H. Chang, and J. Yang. “Sparse deep stacking network for image classification.” 2015
 O. Russakovsky et al., “Imagenet large scale visual recognition challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015.
 G. E. Dahl, D. Yu, L. Deng, and A. Acero, “Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition,” IEEE Trans. Audio Speech Lang. Process., vol. 20, no. 1, pp. 30–42, Jan. 2012.
 L. Deng, D. Yu, and J. Platt, “Scalable stacking and learning for building deep architectures,” in Proc. Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Mar. 2012, pp. 2133–2136.
 Q. Liu, R. Hang, H. Song, and Z. Li, “Learning multiscale deep features for high-resolution satellite image scene classification,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 1, pp. 117–126, Jan. 2018.
 X. Li, Z. Yuan, and Q. Wang, “Unsupervised deep noise modeling for hyperspectral image change detection,” Remote Sens., vol. 11, no. 3, p. 258, Jan. 2019.
 M. E. Paoletti et al., “Capsule networks for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 4, pp. 2145–2160, Apr. 2019.
 K. Zhu, Y. Chen, P. Ghamisi, X. Jia, and J. A. Benediktsson, “Deep convolutional capsule network for hyperspectral image spectral and spectral-spatial classification,” Remote Sens., vol. 11, no. 3, p. 223, Jan. 2019.
 Guo, Y.; Pan, Z.; Wang, M.; Wang, J.; Yang, W. Learning Capsules for SAR Target Recognition. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 4663–4673.
 Lee, J.S.; Pottier, E. Polarimetric Radar Imaging: From Basics to Applications; CRS Press: Boca Raton, FL, USA, 2009.
 Ziegler, V.; Lüneburg, E.; Schroth, A. Mean backscattering properties of random radar targets-A polarimetric covariance matrix concept. In Proceedings of the IGARSS’92; Proceedings of the 12th Annual Int Geo and Remote Sensing Symposium, Houston, TX, USA, 26–29 May 1992; Volume 1, pp. 266–268.
 N. Anfinisen, R. Jenssen, and T. Eltoft, “Spectral clustering of polarimetric SAR data with Wishart-derived distance measures,” in Proc. POLInSAR 2007, Esrin
 Barron, J.T.: A generalization of otsu’s method and minimum error thresholding. ECCV (2020).