Abstract
A novel and effective change detection method based on manifold regularized joint non-negative matrix factorization (MJNMF) framework is proposed in this paper, which detects the changes that occurred in multi-temporal remote sensing images. Most change detection methods, including dictionary learning, principal component analysis (PCA), etc., do not consider the non-negativity among image pixels. However, image itself is a non-negative signal, and the non-negative constraint has better interpretability in practical applications. Nonnegative Matrix Factorization, which incorporates the non-negativity constraint and thus learns object parts, obtains the parts-based representation as well as enhancing the interpretability of the issue correspondingly. In this paper, our proposed approach based on MJNMF framework aims to establish a pair of joint basis matrices by unchanged training samples from unchanged area. Then, unchanged pixels can be well reconstructed by the corresponding basis matrix, while changed pixels cannot be reconstructed from the basis matrix corresponding to the knowledge of unchanged samples, or a larger reconstruction error can be generated even if changed pixels are reconfigurable. In order to suppress similar information and highlight different information, the cross-reconstruction error is used to generate the difference image. Finally, the binary image is obtained by the robust fuzzy local information c-means (FLICM) clustering algorithm. In addition, inspired by manifold learning, we incorporate manifold regularization into the proposed method to keep the geometric structure of data and improve the accuracy of change detection. Experimental results obtained on simulated and real remote sensing images confirm the effectiveness of the proposed method.
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References
Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv Neural Inf Proces Syst 6:585–591
Bezdek JC, Ehrlich R, Full W (1984) Fcm: the fuzzy c-means clustering algorithm. Comput Geosci 2:191–203
Bruzzone L, Cossu R (2003) An adaptive approach to reducing registration noise effects in unsupervised change detection. IEEE Trans Geosci Remote Sens 11:2455–2465
Bruzzone L, Serpico SB (1997) An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images. IEEE Trans Geosci Remote Sens 4:858–867
Bujor F, Trouve E, Valet L (2004) Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images. IEEE Trans Geosci Remote Sens 10:2073–2084
Celik T (2009) Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geosci Remote Sens Lett 4:772–776
Chen H, Shi Z (2020) A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens 12(10):1662
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 1:35–46
Dianat R, Kasaei S (2009) Change detection in optical remote sensing images using difference-based methods and spatial information. IEEE Geosci Remote Sens Lett 1:215–219
Du B, Ru LX, Wu C, Zhang LP (2019) Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images. IEEE Trans Geosci Remote Sens 57(12):9976–9992
Facchinei F, Kanzow C, Sagratella S (2014) Solving quasi-variational inequalities via their KKT conditions. Math Program 2:369–412
Gao F, Dong JY, Li B, Xu Q (2016) Automatic change detection in synthetic aperture radar images based on PCANet. IEEE Geosci Remote Sens Lett 13(12):1792–1796
Gao F, Liu XP, Dong JY, Jian MW (2017) Change detection in SAR images based on deep semi-NMF and SVD networks. Remote Sens 5:435–455
Gao F, Wang X, Gao YH, Dong JY, Wang SK (2019) Sea ice change detection in SAR images based on convolutional-wavelet neural networks. IEEE Geosci Remote Sens Lett 16(8):1240–1244
Ghosh S, Bruzzone L, Patra S, Bovolo F, Ghosh A (2007) A context-sensitive technique for unsupervised change detection based on hopfield-type neural networks. IEEE Trans Geosci Remote Sens 3:778–789
Gong MG, Cao Y, Wu Q (2011) A neighborhood-based ratio approach for change detection in SAR images. IEEE Geosci Remote Sens Lett 2:307–311
Gong M, Zhao J, Liu J, Miao Q, Jiao L (2015) Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans Neural Netw Learn Syst 27(1):125–138
Gong MG, Zhang P, Su LZ (2016) Coupled dictionary learning for change detection from multisource data. IEEE Trans Geosci Remote Sens 12:7077–7091
Gupta N, Ari S, Panigrahi N (2019) Change detection in Landsat images using unsupervised learning and RBF-based clustering. IEEE Trans Emerg Top Comput Intell 5:284–297. https://doi.org/10.1109/TETCI.2019.2932087
Hou B, Liu Q, Wang H, Wang Y (2019) From W-net to CDGAN: Bitemporal change detection via deep learning techniques. IEEE Trans Geosci Remote Sens 58(3):1790–1802
Hussain M, Chen D, Cheng A (2013) Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J Photogramm Remote Sens 2:91–106
Krinidis S, Chatzis V (2010) A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 5:1328–1337
Kwon K, Shin JW, Kim NS (2015) Target source separation based on discriminative nonnegative matrix factorization incorporating cross-reconstruction error. IEICE Trans Inf Syst 11:2017–2020
Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 6755:788–791
Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization. Proceedings of the 13th International Conference on Neural Information Processing Systems 2: 556-562
Li L, Zhao YQ, Sun JJ, Stolkin R, Liu ZG (2018) Deformable dictionary learning for SAR image change detection. IEEE Trans Geosci Remote Sens 99:1–13
Liu J, Gong MG, Zhao JJ, Li H, Jiao LC (2016) Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images. Soft Comput 12:4645–4657
Liu R, Jiang D, Zhang L, Zhang Z (2020) Deep depthwise separable convolutional network for change detection in optical aerial images. IEEE J Sel Top Appl Earth Obs Remote Sens 13:1109–1118
Lu XQ, Yuan Y, Zheng XT (2017) Joint dictionary learning for multispectral change detection. IEEE Trans Cybern 4:884–897
Marchesi S, Bruzzone L (2009) ICA and kernel ICA for change detection in multispectral remote sensing images. Proc IEEE Int Geosci Remote Sens Symp 2:980–983
Onur I, Maktav D, Sari M (2009) Change detection of land cover and land use using remote sensing and GIS: a case study in Kemer, Turkey. Int J Remote Sens 7:1749–1757
Ridd MK, Liu J (1998) A comparison of four algorithms for change detection in an urban environment. Remote Sens Environ 2:95–100
Rokni K, Ahmad A, Selamat A (2014) Water feature extraction and change detection using multitemporal Landsat imagery. Remote Sens 5:4173–4189
Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 5500:2323–2326
Tenenbaum J, Silva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 5500:2319–2323
Ullman S (1997) High-level vision: object recognition and visual cognition. Opt Eng 5:231–256
Yan W, Shi S, Pan L, Zhang G, Wang L (2018) Unsupervised change detection in SAR images based on frequency difference and a modified fuzzy c-means clustering. Int J Remote Sens 39(10):3055–3075
Zhang X, Zheng Y, Feng J (2012) SAR image change detection based on low rank matrix decomposition. Proc IEEE Int Geosci Remote Sens Symp 12:6271–6274
Acknowledgements
This research was funded by the National Natural Science Foundation of China (grant number 61201323), Natural Science Foundation projects of Shaanxi Province (grant number 2017JM6026, 2018JM6050), and the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University (grant number ZZ2019211).
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Communicated by: H. Babaie
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Yan, W., Liu, X., Wen, J. et al. Change detection in remote sensing images based on manifold regularized joint non-negative matrix factorization. Earth Sci Inform 14, 1763–1776 (2021). https://doi.org/10.1007/s12145-021-00620-7
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DOI: https://doi.org/10.1007/s12145-021-00620-7