Abstract
Satellite image classification plays an important role in many fields. It can be divided into two groups: supervised and unsupervised classification. The former requires a large number of labeled data, while, in practice, satellite images usually lack of sufficient ones. How to achieve high accuracy on unsupervised satellite image classification is a key problem. For tackling this problem, based on the idea of partial transfer learning, we propose an novel end-to-end unsupervised classification method called coordinate partial adversarial domain adaptation (CPADA) for satellite image classification. Under the aid of a novel coordinate loss, our framework transfers relevant examples in the shared classes to promote performance, and ignore irrelevant ones in the specific classes to mitigate negative transfer. Experiments show that our CPADA exceeds state-of-the-art results for unsupervised satellite image classification task.
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References
Tyagi M, Bovolo F, Mehra AK, Chaudhuri S, Bruzzone L (2008) A context-sensitive clustering technique based on graph-cut initialization and expectationmaximization algorithm. IEEE Geosci Remote Sens Lett 5(1):2125
Rizvi IA, Mohan BK (2011) Object-based image analysis of high-resolution satellite images using modified cloud basis function neural network and probabilistic relaxation labeling process. IEEE Trans Geosci Remote Sens 49(12):48154820
Aksoy S, Koperski K, Tusk C, Marchisio G, Tilton JC (2005) Learning Bayesian classifiers for scene classification with a visual grammar. IEEE Trans Geosci Remote Sens 43(3):581589
Bellens R, Gautama S, Martinez-Fonte L, Philips W, Chan JCW, Canters F (2008) Improved classification of VHR images of urban areas using directional morphological profiles. IEEE Trans Geosci Remote Sens 46(10):28032813
Zeiler MD, Taylor GW, Fergus R (2011) Adaptive deconvolutional networks for mid and high-level feature learning. In: Proceedings of the 2015 IEEE international conference on computer vision, November 2011. IEEE Computer Society, Washington, DC, p 20182025
Penatti OAB, Nogueira K, dos Santos JA (2015) Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, June 2015. IEEE Computer Society, Washington, DC p 4451
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE Computer Society, Washington, DC
Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky VS (2016) Domain-adversarial training of neural networks. J Mach Learn Res 59(159):35
Long M, Zhu H, Wang J, Jordan MI (2016) Unsupervised domain adaptation with residual transfer networks. In: Advances in neural information processing systems. MIT Press, Cambridge, p 136144
Long M, Cao Y, Wang J Jordan MI (2015) Learning transferable features with deep adaptation networks. In: International conference on machine learning (ICML). Association for Computing Machinery, New York
Zeiler MD, Krishnan D, Taylor GW, Fergus R (2010) Deconvolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, June 2010. IEEE Computer Society, Washington, DC, p 25282535
Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):17981828
Zhang J, Ding Z, Li W, Ogunbona P (2018) Importance weighted adversarial nets for partial domain adaptation. In: IEEE conference on computer vision and pattern recognition (CVPR), June. IEEE Computer Society, Washington, DC
Cheriyadat AM (2014) Unsupervised feature learning for aerial scene classification. IEEE Trans Geosci Remote Sens 52(1):439451
Cao Z, Long M, Wang J, Jordan MI (2018) Partial transfer learning with selective adversarial networks. In: IEEE conference on computer vision and pattern recognition (CVPR), June. IEEE Computer Society, Washington, DC
Cao Z, Ma L, Long M, Wang J (2018) Partial adversarial domain adaptation. In: European conference on computer vision (ECCV), September. Springer, New York
Castelluccio M, Poggi G, Sansone C, Verdoliva L (2015) Land use classification in remote sensing images by convolutional neural networks [Online]. arxiv:1508.00092
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE Computer Society, Washington, DC
Pan SJ, Tsang IW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw Learn Syst 22(2):199210
Hu F, Xia G-S, Wang Z, Huang X, Zhang L, Sun H (2015) Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification. IEEE J Sel Top Appl Earth Observ Remote Sens 8(5):20152030
Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2015) Simultaneous deep transfer across domains and tasks. In: IEEE international conference on computer vision (ICCV). IEEE Computer Society, Washington, DC
Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: maximizing for domain invariance, arXiv:1412.3474 [cs.CV]
Acknowledgements
This work is jointly supported by National Natural Science Foundation of China (Grant Nos. 61673262 and 61175028) and Shanghai key project of basic research (Grant No. 16JC1401100).
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Hu, J., Tuo, H., Wang, C. et al. Unsupervised satellite image classification based on partial transfer learning. AS 3, 21–28 (2020). https://doi.org/10.1007/s42401-019-00038-6
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DOI: https://doi.org/10.1007/s42401-019-00038-6