Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping

International Journal of Remote Sensing

Volumn 37, Issue 23, 2016, Pages 5632-5646

  Li, Weijia ^a,b   Fu, Haohuan ^a,b   Yu, Le ^a,b   Gong, Peng ^a,b   Feng, Duole ^a,b   Li, Congcong ^a,b   Clinton, Nicholas ^a,b  

^a TSINGHUA UNIVERSITY   (China)

^b JOINT CENTER FOR GLOBAL CHANGE STUDIES JCGCS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; DEEP NEURAL NETWORKS; IMAGE CLASSIFICATION; LEARNING ALGORITHMS; MAXIMUM LIKELIHOOD; NEURAL NETWORKS; REMOTE SENSING; SAMPLING; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; CLASSIFICATION ALGORITHM; CLASSIFICATION FRAMEWORK; CLASSIFICATION METHODS; MAXIMUM LIKELIHOOD CLASSIFIERS; REMOTE SENSING IMAGE CLASSIFICATION; REMOTE SENSING IMAGE PROCESSING; STACKED AUTOENCODER;

MAPPING;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; IMAGE CLASSIFICATION; LAND COVER; MAPPING; OPTIMIZATION; REMOTE SENSING;

EID: 84993997577     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2016.1246775     Document Type: Article

References (31)

1. F.Bastien, P.Lamblin, R.Pascanu, J.Bergstra, I.Goodfellow, A.Bergeron, N.Bouchard, D.Warde-Farley, and Y.Bengio. 2012. “Theano:New Features and Speed Improvements.” In Paper presented at the NIPS 2012 Deep Learning Workshop, Lake Tahoe, December 8.
2. J.Bergstra, O.Breuleux, F.Bastien, P.Lamblin, R.Pascanu, G.Desjardins, J.Turian, D.Warde-Farley, and Y.Bengio. 2010. “Theano:A CPU and GPU Math Expression Compiler.” Paper presented at the Proceedings of the Python for Scientific Computing Conference (SciPy), Austin, TX, June 28–July 3.
3. L.Bottou, 2010. “Large-Scale Machine Learning with Stochastic Gradient Descent.” Paper presented at the International Conference on Computational Statistics, Paris, August 22–27.
4. Y.Chen, Z.Lin, X.Zhao, G.Wang, and Y.Gu. 2014. “Deep Learning-Based Classification of Hyperspectral Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6):2094–2107. doi:10.1109/JSTARS.2014.2329330.
5. Y.Chen, X.Zhao, and X.Jia. 2015. “Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (6):1–12. doi:10.1109/JSTARS.2015.2388577.
6. G.Cheng, and J.Han. 2016. “A Survey on Object Detection in Optical Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 117:11–28. doi:10.1016/j.isprsjprs.2016.03.014.
7. G.Cheng, J.Han, L.Guo, Z.Liu, S.Bu, and J.Ren. 2015. “Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 53 (8):4238–4249. doi:10.1109/TGRS.2015.2393857.
8. G.Cheng, J.Han, P.Zhou, and L.Guo. 2014. “Multi-Class Geospatial Object Detection and Geographic Image Classification Based on Collection of Part Detectors.” ISPRS Journal of Photogrammetry and Remote Sensing 98:119–132. doi:10.1016/j.isprsjprs.2014.10.002.
9. D.L.Civco, 1993. “Artificial Neural Networks for Land-Cover Classification and Mapping.” International Journal of Geographical Information Science 7 (2):173–186. doi:10.1080/02693799308901949.
10. R.Collobert, and J.Weston. 2008. “A Unified Architecture for Natural Language Processing:Deep Neural Networks with Multitask Learning.” Paper presented at the International Conference on Machine learning, Helsinki, July 5–9.
11. G.E.Dahl, D.Yu, L.Deng, and A.Acero. 2012. “Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition.” IEEE Transactions on Audio, Speech, and Language Processing 20 (1):30–42. doi:10.1109/TASL.2011.2134090.
12. J.Gehring, Y.Miao, F.Metze, and A.Waibel. 2013. “Extracting Deep Bottleneck Features Using Stacked Auto-Encoders.” Paper presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, May 26–31.
13. P.Gong, J.Wang, L.Yu, Y.C.Zhao, Y.Y.Zhao, L.Liang, Z.G.Niu, et al. 2013. “Finer Resolution Observation and Monitoring of Global Land Cover:First Mapping Results with Landsat TM and ETM+ Data.” International Journal of Remote Sensing 34:2607–2654. doi:10.1080/01431161.2012.748992.
14. J.Han, D.Zhang, G.Cheng, L.Guo, and J.Ren. 2015. “Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning.” IEEE Transactions on Geoscience and Remote Sensing 53 (6):3325–3337. doi:10.1109/TGRS.2014.2374218.
15. G.Hinton, L.Deng, D.Yu, G.E.Dahl, A.-R.Mohamed, N.Jaitly, A.Senior, et al. 2012. “Deep Neural Networks for Acoustic Modeling in Speech Recognition:The Shared Views of Four Research Groups.” IEEE Signal Processing Magazine 29 (6):82–97. doi:10.1109/MSP.2012.2205597.
16. A.Krizhevsky, I.Sutskever, and G.E.Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” Paper presented at the Annual Conference on Neural Information Processing Systems, Nevada, December 3–6.
17. Q.V.Le, 2013. “Building High-Level Features Using Large Scale Unsupervised Learning.” Paper presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, May 26–31.
18. H.Lee, R.Grosse, R.Ranganath, and A.Y.Ng. 2009. “Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations.” Paper presented at the International Conference on Machine Learning, Montreal, June 14–18.
19. C.Li, P.Gong, J.Wang, C.Yuan, T.Y.Hu, Q.Wang, L.Yu, et al. 2016. “An All-Season Sample Database for Improving Land-Cover Mapping of Africa with Two Classification Schemes.” International Journal of Remote Sensing 37 (19):4623–4647. doi:10.1080/01431161.2016.1213923.
20. C.Li, J.Wang, L.Wang, L.Hu, and P.Gong. 2014. “Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery.” Remote Sensing 6 (2):964–983. doi:10.3390/rs6020964.
21. Q.Lv, Y.Dou, X.Niu, J.Xu, J.Xu, and F.Xia. 2015. “Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks.” Journal of Sensors 501:538063.
22. A.R.Mohamed, T.N.Sainath, G.Dahl, B.Ramabhadran, G.E.Hinton, and M.Picheny. 2011. “Deep Belief Networks Using Discriminative Features for Phone Recognition.” Paper presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, May 22–27.
23. F.Pedregosa, G.Varoquaux, A.Gramfort, V.Michel, B.Thirion, O.Grisel, M.Blondel, et al. 2011. “Scikit-Learn:Machine Learning in Python.” The Journal of Machine Learning Research 12:2825–2830.
24. J.E.Shore, and R.W.Johnson. 1980. “Axiomatic Derivation of the Principle of Maximum Entropy and the Principle of Minimum Cross-Entropy.” IEEE Transactions on Information Theory 26 (1):26–37. doi:10.1109/TIT.1980.1056144.
25. I.Sutskever, O.Vinyals, and Q.V.Le. 2014. “Sequence to Sequence Learning with Neural Networks.” Paper presented at the Annual Conference on Neural Information Processing Systems, Montreal, December 8–13.
26. P.Vincent, H.Larochelle, I.Lajoie, Y.Bengio, and P.A.Manzagol. 2010. “Stacked Denoising Autoencoders:Learning Useful Representations in a Deep Network with a Local Denoising Criterion.” The Journal of Machine Learning Research 11:3371–3408.
27. H.Xu, 2006. “Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery.” International Journal of Remote Sensing 27 (14):3025–3033. doi:10.1080/01431160600589179.
28. L.Yu, L.Liang, J.Wang, Y.Zhao, Q.Cheng, L.Hu, S.Liu, et al. 2014a. “Meta-Discoveries from a Synthesis of Satellite-Based Land Cover Mapping Research.” International Journal of Remote Sensing 35 (13):4573–4588. doi:10.1080/01431161.2014.930206.
29. L.Yu, J.Wang, and P.Gong. 2013. “Improving 30 Meter Global Land Cover Map FROM-GLC with Time Series MODIS and Auxiliary Datasets:A Segmentation Based Approach.” International Journal of Remote Sensing 34 (16):5851–5867. doi:10.1080/01431161.2013.798055.
30. L.Yu, J.Wang, X.Li, C.Li, Y.Zhao, and P.Gong. 2014b. “A Multi-Resolution Global Land Cover Dataset through Multisource Data Aggregation.” Science China Earth Sciences 57 (10):2317–2329. doi:10.1007/s11430-014-4919-z.
31. W.Zhao, Z.Guo, J.Yue, X.Zhang, and L.Luo. 2015. “On Combining Multiscale Deep Learning Features for the Classification of Hyperspectral Remote Sensing Imagery.” International Journal of Remote Sensing 36 (13):3368–3379. doi:10.1080/2150704X.2015.1062157.