A multi-level weighted transformation based neuro-fuzzy domain adaptation technique using stacked auto-encoder for land-cover classification

References

• Alajlan, N., E. Pasolli, F. Melgani, and A. Franzoso. 2014. “Large-scale Image Classification Using Active Learning.” IEEE Geoscience and Remote Sensing Letters 11 (1): 259–263. doi:10.1109/LGRS.2013.2255258.
   Web of Science ®Google Scholar
• Ammour, N., L. Bashmal, Y. Bazi, M. M. Al Rahhal, and M. Zuair. 2018. “Asymmetric Adaptation of Deep Features for Cross-Domain Classification in Remote Sensing Imagery.” IEEE Geoscience and Remote Sensing Letters 15 (4): 597–601. doi:10.1109/LGRS.2018.2800642.
   Web of Science ®Google Scholar
• Azizzadenesheli, K., A. Liu, F. Yang, and A. Anandkumar. 2019. “Regularized Learning for Domain Adaptation under Label Shifts.” In International Conference on Learning Representations, IEEE, New Orleans, USA.
   Google Scholar
• Banerjee, B., F. Bovolo, A. Bhattacharya, L. Bruzzone, S. Chaudhuri, and K. M. Buddhiraju. 2015. “A Novel Graph-Matching-Based Approach for Domain Adaptation in Classification of Remote Sensing Image Pair.” IEEE Transactions on Geoscience and Remote Sensing 53 (7): 4045–4062. doi:10.1109/TGRS.2015.2389520.
   Web of Science ®Google Scholar
• Banerjee, B., and K. M. Buddhiraju. 2016. “Domain Adaptation in the Absence of Source Domain Labeled Samples—A Coclustering-Based Approach.” IEEE Geoscience and Remote Sensing Letters 13 (12): 1905–1909. doi:10.1109/LGRS.2016.2617199.
   Web of Science ®Google Scholar
• Bhattacharyya, A. 1946. “On a Measure of Divergence between Two Multinomial Populations.” Sankhya: The Indian Journal of Statistics (1933–1960) 7 (4): 401–406.
   Google Scholar
• Cao, Z., K. You, M. Long, J. Wang, and Q. Yang. 2019. “Learning to Transfer Examples for Partial Domain Adaptation.” In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, California, USA.
   Google Scholar
• Chakraborty, S., and M. Roy. 2018. “A Neural Approach under Transfer Learning for Domain Adaptation in Land-cover Classification Using Two-level Cluster Mapping.” Applied Soft Computing 64: 508–525. doi:10.1016/j.asoc.2017.12.018.
   Web of Science ®Google Scholar
• Chang, C.-C., and C.-J. Lin. 2011. “LIBSVM: A Library for Support Vector Machines.” ACM Transactions on Intelligent Systems and Technology 27 (2): 1–27. doi:10.1145/1961189.1961199.
   Google Scholar
• Chen, C., W. Xie, W. Huang, Y. Rong, X. Ding, Y. Huang, T. Xu, and J. Huang. 2019. “Progressive Feature Alignment for Unsupervised Domain Adaptation.” In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, California, USA.
   Google Scholar
• Chen, Y., S. Song, S. Li, L. Yang, and C. Wu. 2018. “Domain Space Transfer Extreme Learning Machine for Domain Adaptation.” IEEE Transactions on Cybernetics 49: 1–14.
   Web of Science ®Google Scholar
• Clinchant, S., G. Csurka, and B. Chidlovskii. 2016. “A Domain Adaptation Regularization for Denoising Autoencoders.” In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Vol. 2, 26–31, Berlin.
   Google Scholar
• Davis, J. V., B. Kulis, P. Jain, S. Sra, and I. S. Dhillon. 2007. “Information-theoretic Metric Learning.” In 24th ACM International Conference on Machine Learning, New York, 209–216.
   Google Scholar
• Dey, S., S. Madikeri, and P. Motlicek. 2016. “Information Theoretic Clustering for Unsupervised Domain-adaptation.” In IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China, March, 5580–5584.
   Google Scholar
• Ding, Z., M. Shao, and F. Yun 2015. “Deep Low-rank Coding for Transfer Learning.” In Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina.
   Google Scholar
• Ding, Z., L. Sheng, M. Shao, and F. Yun 2018. “Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation.” In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 37–52.
   Google Scholar
• Farshchian, A., J. A. Gallego, J. P. Cohen, Y. Bengio, L. E. Miller, and S. A. Solla. 2019. “Adversarial Domain Adaptation for Stable Brain-Machine Interfaces.” In International Conference on Learning Representations (ICLR), May, New Orleans, USA.
   Google Scholar
• Fernando, B., A. Habrard, M. Sebban, and T. Tuytelaars. 2013. “Unsupervised Visual Domain Adaptation Using Subspace Alignment.” In IEEE International Conference on Computer Vision, Sydney, December, 2960–2967.
   Google Scholar
• Ganin, Y., and V. Lempitsky. 2015a. “Unsupervised Domain Adaptation by Backpropagation.” In the Proceedings of 32nd International Conference on Machine Learning, France, 1180–1189.
   Google Scholar
• Ganin, Y., and V. Lempitsky. 2015b. “Unsupervised Domain Adaptation by Backpropagation.” In Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 1180–1189.
   Google Scholar
• Ghifary, M., W. Bastiaan Kleijn, M. Zhang, D. Balduzzi, and L. Wen 2016. “Deep Reconstruction-classification Networks for Unsupervised Domain Adaptation.” In European Conference on Computer Vision, 597–613. Amsterdam, The Netherlands: Springer.
   Google Scholar
• Glorot, X., A. Bordes, and Y. Bengio. 2011. “Domain Adaptation for Large-scale Sentiment Classification: A Deep Learning Approach.” In Proceedings of the 28th International Conference on Machine Learning (ICML-11), Washington, USA, 513–520.
   Google Scholar
• Gong, B., Y. Shi, F. Sha, and K. Grauman. 2012. “Geodesic Flow Kernel for Unsupervised Domain Adaptation.” In IEEE Conference on Computer Vision and Pattern Recognition, Providence, June, 2066–2073.
   Google Scholar
• Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep Learning. MIT Press. https://www.deeplearningbook.org/ for details
   Google Scholar
• Gopalan, R., R. Li, and R. Chellappa. 2014. “Unsupervised Adaptation across Domain Shifts by Generating Intermediate Data Representations.” IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (11): 2288–2302. doi:10.1109/TPAMI.2013.249.
   PubMed Web of Science ®Google Scholar
• Guha, R. A., and D. Sheet. 2016. “DASA: Domain Adaptation in Stacked Autoencoders Using Systematic Dropout.” In the Proceedings of the 2nd Indian Workshop on Machine Learning, IIT Kanpur, India.
   Google Scholar
• Guo, Q., B. Zhang, Q. Ran, L. Gao, J. Li, and A. Plaza. 2014. “Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics Estimation for Anomaly Detection in Hyperspectral Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2351–2366. doi:10.1109/JSTARS.2014.2302446.
   Web of Science ®Google Scholar
• Haykin, S. 2007. Neural Networks: A Comprehensive Foundation. New Delhi: Prentice-Hall of India.
   Google Scholar
• Hou, H., J. Huo, and Y. Gao. 2018. “Cross-Domain Adversarial Auto-Encoder.” https://arxiv.org/abs/1804.06078 for details
   Google Scholar
• Imani, M., and H. Ghassemian. 2015. “Feature Extraction Using Weighted Training Samples.” IEEE Geoscience and Remote Sensing Letters 12 (7): 1386–1387. doi:10.1109/LGRS.2015.2402167.
   Web of Science ®Google Scholar
• Jiang, J. 2008. “A Literature Survey on Domain Adaptation of Statistical Classifiers.” URL: http://sifaka. cs. uiuc. edu/jiang4/domainadaptation/survey
   Google Scholar
• Kang, G., L. Jiang, Y. Yang, and A. G. Hauptmann. 2019. “Contrastive Adaptation Network for Unsupervised Domain Adaptation.” In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, California, USA.
   Google Scholar
• Kim, M., J. Lee, D. Han, M. Shin, J. Im, J. Lee, L. J. Quackenbush, and Z. Gu. 2018. “Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (12): 4604–4617. doi:10.1109/JSTARS.2018.2880783.
   Web of Science ®Google Scholar
• Lee, S., D. Kim, N. Kim, and S. Jeong. 2019. “Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation.” In The IEEE International Conference on Computer Vision (ICCV), October, California, USA.
   Google Scholar
• Li, S., S. Song, and G. Huang. 2017. “Prediction Reweighting for Domain Adaptation.” IEEE Transactions on Neural Networks and Learning Systems 28 (7): 1682–1695. doi:10.1109/TNNLS.2016.2538282.
   PubMed Web of Science ®Google Scholar
• Liang, J., R. He, Z. Sun, and T. Tan. 2019. “Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June. California, USA, IEEE.
   Google Scholar
• Liu, H., Z. Cao, M. Long, J. Wang, and Q. Yang. 2019. “Separate to Adapt: Open Set Domain Adaptation via Progressive Separation.” In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, California, USA.
   Google Scholar
• Ma, L., and J. Song. 2017. “Deep Neural Network-based Domain Adaptation for Classification of Remote Sensing Images.” Journal of Applied Remote Sensing 11 (4): 1–19. doi:10.1117/1.JRS.11.042612.
   Google Scholar
• Maaten, L., and G. Hinton. 2008. “Visualizing Data Using t-SNE.” Journal of Machine Learning Research 9 (Nov): 2579–2605.
   Google Scholar
• Matasci, G., M. Volpi, M. Kanevski, L. Bruzzone, and D. Tuia. 2015. “Semisupervised Transfer Component Analysis for Domain Adaptation in Remote Sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 53 (7): 3550–3564. doi:10.1109/TGRS.2014.2377785.
   Web of Science ®Google Scholar
• Meher, S. K., and D. A. Kumar. 2015. “Ensemble of Adaptive Rule-Based Granular Neural Network Classifiers for Multispectral Remote Sensing Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (5): 2222–2231. doi:10.1109/JSTARS.2015.2403297.
   Web of Science ®Google Scholar
• Nozza, D., E. Fersini, and E. Messina. 2016. “Deep Learning and Ensemble Methods for Domain Adaptation.” In the Proceedings of IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), San Jose, USA, 184–189.
   Google Scholar
• Pan, S. J., I. W. Tsang, J. T. Kwok, and Q. Yang. 2011. “Domain Adaptation via Transfer Component Analysis.” IEEE Transactions on Neural Networks 22 (2): 199–210. doi:10.1109/TNN.2010.2091281.
   PubMed Web of Science ®Google Scholar
• Pan, S. J., and Q. Yang. 2010. “A Survey on Transfer Learning.” IEEE Transactions on Knowledge and Data Engineering 22 (10): 1345–1359. doi:10.1109/TKDE.2009.191.
   Web of Science ®Google Scholar
• Patel, V. M., R. Gopalan, R. Li, and R. Chellappa. 2015. “Visual Domain Adaptation: A Survey of Recent Advances.” IEEE Signal Processing Magazine 32 (3): 53–69. doi:10.1109/MSP.2014.2347059.
   Web of Science ®Google Scholar
• Persello, C. 2013. “Interactive Domain Adaptation for the Classification of Remote Sensing Images Using Active Learning.” IEEE Geoscience and Remote Sensing Letters 10 (4): 736–740. doi:10.1109/LGRS.2012.2220516.
   Web of Science ®Google Scholar
• Persello, C., and L. Bruzzone. 2012. “Active Learning for Domain Adaptation in the Supervised Classification of Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 50 (11): 4468–4483. doi:10.1109/TGRS.2012.2192740.
   Web of Science ®Google Scholar
• Roy, M., F. Melgani, A. Ghosh, E. Blanzieri, and S. Ghosh. 2015. “Land-Cover Classification of Remotely Sensed Images Using Compressive Sensing Having Severe Scarcity of Labeled Patterns.” IEEE Geoscience and Remote Sensing Letters 12 (6): 1257–1261. doi:10.1109/LGRS.2015.2391297.
   Web of Science ®Google Scholar
• Saha, A., P. Rai, H. Daumé, I. I. I., . S. Venkatasubramanian, and S. L. DuVall. 2011. “Active Supervised Domain Adaptation.” In the Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Italy, 97–112.
   Google Scholar
• Sharma, S., K. M. Buddhiraju, and B. Banerjee. 2014. “An Ant Colony Optimization Based Inter Domain Cluster Mapping for Domain Adaptation in Remote Sensing.” In the Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2158–2161. Quebec City, Canada.
   Google Scholar
• Shi, Q., B. Du, and L. Zhang. 2015. “Domain Adaptation for Remote Sensing Image Classification: A Low-Rank Reconstruction and Instance Weighting Label Propagation Inspired Algorithm.” IEEE Transactions on Geoscience and Remote Sensing 53 (10): 5677–5689. doi:10.1109/TGRS.2015.2427791.
   Web of Science ®Google Scholar
• Tuia, D., E. Pasolli, and W. J. Emery. 2011. “Using Active Learning to Adapt Remote Sensing Image Classifiers.” Remote Sensing of Environment 115 (9): 2232–2242. doi:10.1016/j.rse.2011.04.022.
   Web of Science ®Google Scholar
• Tuia, D., C. Persello, and L. Bruzzone. 2016. “Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances.” IEEE Geoscience and Remote Sensing Magazine 4 (2): 41–57. doi:10.1109/MGRS.2016.2548504.
   Web of Science ®Google Scholar
• Vincent, P., H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol. 2010. “Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion.” Journal of Machine Learning Research 11: 3371–3408.
   Web of Science ®Google Scholar
• Xu, R., G. Li, J. Yang, and L. Lin. 2019. “Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation.” In Proceedings of the International Conference on Computer Vision (ICCV), October. Seoul, Korea, IEEE.
   Google Scholar
• Zadeh, L. A. 1975. “The Concept of a Linguistic Variable and Its Application to Approximate reasoning.” Information Sciences 8 (3): 199–249. doi:10.1016/0020-0255(75)90036-5.
   Web of Science ®Google Scholar
• Zhang, L., L. Zhang, and B. Du. 2016. “Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art.” IEEE Geoscience and Remote Sensing Magazine 4 (2): 22–40. doi:10.1109/MGRS.2016.2540798.
   Web of Science ®Google Scholar
• Zhu, R., L. Yan, N. Mo, and Y. Liu. 2019. “Semi-supervised Center-based Discriminative Adversarial Learning for Cross-domain Scene-level Land-cover Classification of Aerial Images.” ISPRS Journal of Photogrammetry and Remote Sensing 155: 72–89. doi:10.1016/j.isprsjprs.2019.07.001.
   Web of Science ®Google Scholar
• Ziser, Y., and R. Reichart. 2017. “Neural Structural Correspondence Learning for Domain Adaptation.” 400–410. https://arxiv.org/abs/1610.01588 for details
   Google Scholar