[1]LEI Tao,WANG Jie,XUE Dinghua,et al.Unsupervised SAR image change detection based on difference feature fusion[J].CAAI Transactions on Intelligent Systems,2021,16(3):595-604.[doi:10.11992/tis.202103011]
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Unsupervised SAR image change detection based on difference feature fusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 595-604 Column: 人工智能院长论坛 Public date: 2021-05-05
Title:
Unsupervised SAR image change detection based on difference feature fusion
Author(s):
LEI Tao1; 2;  WANG Jie3;  XUE Dinghua3;  WANG Xingwu2;  DU Xiaogang2
1. School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, China;
2. Shaanxi Joint Laboratory of Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, China;
3. School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
Keywords:
SAR remote sensing image; change detection; unsupervised learning; convolution neural network; feature fusion; difference image; fuzzy c-means; Siamese network
CLC:
TP18
DOI:
10.11992/tis.202103011
Abstract:
The Siamese network ignores the correlation between different levels of different features, leading to limited detection accuracy. To overcome this limitation, an unsupervised synthetic aperture radar (SAR) image change detection algorithm based on the fusion of different features is proposed. The method uses log-ratio and mean-ratio operators to construct two complementary difference images and introduces an energy matrix to perform pixel-level fusion on these difference maps to improve its signal-noise ratio. Next, a Siamese network based on different feature fusion (DFF-Siamese) is designed. By introducing a difference feature extraction module, the DFF-Siamese comprehensively measure the differences and similarities between the multitemporal images using high-level semantic features, effectively improving the feature representation capability of the network. Finally, a fuzzy clustering algorithm is used to classify the fusion images to construct fake labels used to train the developed DFF-Siamese network, achieving accurate change detection for the SAR images. Experimental results from three real remote sensing datasets showed that the proposed method exhibits higher change detection accuracy with lower error rates compared with other popular algorithms.
References:
[1] LI Lu, WANG Chao, ZHANG Hong, et al. Urban building change detection in SAR images using combined differential image and residual U-Net network[J]. Remote sensing, 2019, 11(9):1091.
[2] KURUOGLU E E, ZERUBIA J. Modeling SAR images with a generalization of the Rayleigh distribution[J]. IEEE transactions on image processing, 2004, 13(4):527-533.
[3] WANG Rongfang, ZHANG Jie, CHEN Jiawei, et al. Imbalanced learning-based automatic SAR images change detection by morphologically supervised PCA-Net[J]. IEEE geoscience and remote sensing letters, 2019, 16(4):554-558.
[4] CHEN Hongruixuan, WU Chen, DU Bo, et al. Change detection in multisource VHR images via deep Siamese convolutional multiple-layers recurrent neural network[J]. IEEE transactions on geoscience and remote sensing, 2020, 58(4):2848-2864.
[5] PIRRONE D, BOVOLO F, BRUZZONE L. A novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images[J]. IEEE transactions on geoscience and remote sensing, 2020, 58(7):4780-4795.
[6] XUE Dinghua, LEI Tao, JIA Xiaohong, et al. Unsupervised change detection using multiscale and multiresolution Gaussian-mixture-model guided by saliency enhancement[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2020, 14:1796-1809.
[7] GONG Maoguo, ZHOU Zhiqiang, MA Jingjing. Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering[J]. IEEE transactions on image processing, 2012, 21(4):2141-2151.
[8] JIA Lu, LI Ming, ZHANG Peng, et al. Remote-sensing image change detection with fusion of multiple wavelet kernels[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2016, 9(8):3405-3418.
[9] ZHENG Yaoguo, ZHANG Xiangrong, HOU Biao, et al. Using combined difference image and k-means clustering for SAR image change detection[J]. IEEE geoscience and remote sensing letters, 2014, 11(3):691-695.
[10] 邵攀,任东,董婷.顾及冲突分析的模糊积分非监督变化检测[EB/OL].(2019?07?02)[2021?03?05] https://doi.org/10.16383/j.aas.c190047.
SHAO Pan, REN Dong, DONG Ting. Unsupervised change detection based on fuzzy integral considering conflict analysis[EB/OL]. (2019?07?02)[2021?03?05] https://doi.org/10.16383/j.aas.c190047.
[11] GONG Maoguo, SU Linzhi, JIA Meng, et al. Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images[J]. IEEE transactions on fuzzy systems, 2014, 22(1):98-109.
[12] LEI Tao, XUE Dinghua, LYU Zhiyong, et al. Unsupervised change detection using fast fuzzy clustering for landslide mapping from very high-resolution images[J]. Remote sensing, 2018, 10(9):1381.
[13] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab:semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 40(4):834-848.
[14] LEI Tao, ZHANG Yuxiao, LYU Zhiyong, et al. Landslide inventory mapping from bitemporal images using deep convolutional neural networks[J]. IEEE geoscience and remote sensing letters, 2019, 16(6):982-986.
[15] LIU Jia, GONG Maoguo, QIN Kai, et al. A deep convolutional coupling network for change detection based on heterogeneous optical and radar images[J]. IEEE transactions on neural networks and learning systems, 2018, 29(3):545-559.
[16] ZHAO Wei, WANG Zhirui, GONG Maoguo, et al. Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network[J]. IEEE transactions on geoscience and remote sensing, 2017, 55(12):7066-7080.
[17] GONG Maoguo, ZHAO Jiaojiao, LIU Jia, et al. Change detection in synthetic aperture radar images based on deep neural networks[J]. IEEE transactions on neural networks and learning systems, 2016, 27(1):125-138.
[18] GAO Feng, DONG Junyu, LI Bo, et al. Automatic change detection in synthetic aperture radar images based on PCANet[J]. IEEE geoscience and remote sensing letters, 2016, 13(12):1792-1796.
[19] DONG Huihui, MA Wenping, WU Yue, et al. Local descriptor learning for change detection in synthetic aperture radar images via convolutional neural networks[J]. IEEE access, 2018, 7:15389-15403.
[20] 王昶, 张永生, 韩世静, 等. 基于频域显著性方法和ELM的遥感影像变化检测[J]. 华中科技大学学报(自然科学版), 2020, 48(5):19-24
WANG Chang, ZHANG Yongsheng, HAN Shijing, et al. Remote sensing image change detection based on frequency domain significance method and ELM[J]. Journal of Huazhong University of Science and Technology (natural science edition), 2020, 48(5):19-24
[21] MA Jiayi, MA Yong, LI Chang. Infrared and visible image fusion methods and applications:a survey[J]. Information fusion, 2019, 45:153-178.
[22] ZHANG Z, VOSSELMAN G, GERKE M, et al. Change detection between multimodal remote sensing data using Siamese CNN[EB/OL]. (2018?07?25)[2021?02?11]https://arxiv.org/pdf/1807.09562.
[23] ZAGORUYKO S, KOMODAKIS N. Learning to compare image patches via convolutional neural networks[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA, 2015:4353-4361.
[24] KRINIDIS S, CHATZIS V. A robust fuzzy local information C-means clustering algorithm[J]. IEEE transactions on image processing, 2010, 19(5):1328-1337.
[25] LEI Tao, JIA Xiaohong, ZHANG Yanning, et al. Significantly fast and robust fuzzy C-means clustering algorithm based on morphological reconstruction and membership filtering[J]. IEEE transactions on fuzzy systems, 2018, 26(5):3027-3041.
[26] 雷涛, 张肖, 加小红, 等. 基于模糊聚类的图像分割研究进展[J]. 电子学报, 2019, 47(8):1776-1791
LEI Tao, ZHANG Xiao, JIA Xiaohong, et al. Research progress on image segmentation based on fuzzy clustering[J]. Acta electronica sinica, 2019, 47(8):1776-1791
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Last Update: 2021-06-25

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