References
- Castelluccio, M., G. Poggi, C. Sansone, and L. Verdoliva. 2015. “Land Use Classification in Remote Sensing Images by Convolutional Neural Networks.” Arxiv Preprint Arxiv:1508.00092.
- Chatfield, K., K. Simonyan, A. Vedaldi, and A. Zisserman. 2014. “Return of the Devil in the Details: Delving Deep into Convolutional Nets.” In Proceedings of the British Machine Vision Conference, 1-5 September, University of Nottingham, BMVA Press, arXiv preprint arXiv:1405.3531. DOI: 10.5244/C.28.6.
- Cusano, C., P. Napoletano, and R. Schettini. 2015. “Remote Sensing Image Classification Exploiting Multiple Kernel Learning.” IEEE Geoscience and Remote Sensing Letters 12 (11): 2331–2335. doi:10.1109/LGRS.2015.2476365.
- Foody, G. M. 2004. “Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy.” Photogrammetric Engineering and Remote Sensing 70 (5): 627–633. doi:10.14358/PERS.70.5.627.
- Foody, G. M. 2009. “Classification Accuracy Comparison: Hypothesis Tests and the Use of Confidence Intervals in Evaluations of Difference, Equivalence and Non-Inferiority.” Remote Sensing of Environment 113 (8): 1658–1663. doi:10.1016/j.rse.2009.03.014.
- Hu, F., G. S. Xia, J. Hu, and L. Zhang. 2015. “Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery.” Remote Sensing 7 (11): 14680–14707. doi:10.3390/rs71114680.
- Japkowicz, N., and M. Shah. 2011. Evaluating Learning Algorithms: A Classification Perspective. New York: Cambridge University Press.
- Jia, Y., E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. 2014. “Caffe: convolutional architecture for fast feature embedding.” In Proceedings of the 22nd ACM international conference on Multimedia, ACM, 3-7 November, Orlando, FL, USA, 675-678. DOI:10.1145/2647868.2654889
- Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” Advances in Neural Information Processing Systems 25 (2): 1106–1114.
- Lecun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–444. doi:10.1038/nature14539.
- Lu, D., and Q. Weng. 2007. “A Survey of Image Classification Methods and Techniques for Improving Classification Performance.” International Journal of Remote Sensing 28 (5): 823–870. doi:10.1080/01431160600746456.
- Makantasis, K., K. Karantzalos, A. Doulamis, N. Doulamis. 2015. “Deep supervised learning for hyperspectral data classification through convolutional neural networks.” In IEEE Conference on Internationtional Geoscience and Remote Sensing Symposium (IGARSS), 26-31 July, Milan, Italy, 4959-4962. DOI: 10.1109/IGARSS.2015.7326945.
- Nogueira, K., O. A. B. Penatti, and J. A. D. Santos. 2017. “Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification.” Pattern Recognition 61: 539–556. doi:10.1016/j.patcog.2016.07.001.
- Penatti, O. A. B., K. Nogueira, and J. A. D. Santos. 2015. “Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?” In IEEE Conference on Computer Vision and Pattern Recognition Workshops, 7-12 June, Boston, MA, USA, 44-51. DOI: 10.1109/CVPRW.2015.7301382.
- Risojevic, V., and Z. Babic. 2013. “Fusion of Global and Local Descriptors for Remote Sensing Image Classification.” IEEE Geoscience and Remote Sensing Letters 10 (4): 836–840. doi:10.1109/LGRS.2012.2225596.
- Sermanet, P., D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. 2014. “OverFeat: integrated recognition, localization and detection using convolutional networks.” In Proceedings of the International Conference on Learning Representations, 14-16 April, Banff, Canada, arXiv preprint arXiv:1312.6229.
- Sheng, G., W. Yang, T. Xu, and H. Sun. 2012. “High-Resolution Satellite Scene Classification Using a Sparse Coding Based Multiple Feature Combination.” International Journal of Remote Sensing 33 (8): 2395–2412. doi:10.1080/01431161.2011.608740.
- Simonyan, K. and A. Zisserman. 2015. “Very deep convolutional networks for large-scale image recognition.” In Proceedings of the International Conference on Learning Representations, 7-9 May, San Diego, USA, arXiv preprint arXiv:1409.1556.
- Vedaldi, A., and K. Lenc, 2015. “MatConvNet: convolutional neural networks for MATLAB.” In Proceedings of the 23rd ACM international conference on Multimedia, ACM, 26-30, October, Brisbane, Australia, 689-692. DOI: 10.1145/2733373.2807412.
- Yang, Y. and S. Newsam. 2010. “Bag-of-visual-words and spatial extensions for land-use classification.” In Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, ACM, 2-5 November, San Jose, USA, 270-279. DOI: 10.1145/1869790.1869829.
- Yue, J., W. Zhao, S. Mao, and H. Liu. 2015. “Spectral-Spatial Classification of Hyperspectral Images Using Deep Convolutional Neural Networks.” Remote Sensing Letters 6 (6): 468–477. doi:10.1080/2150704X.2015.1047045.
- Zeiler, M. D. and R. Fergus. 2014. “Visualizing and understanding convolutional networks.” In European Conference on Computer Vision, 6-12 September, Zürich, Switzerland, Springer International Publishing, 818-833. DOI: 10.1007/978-3-319-10590-1_53.
- Zhang, F., B. Du, and L. Zhang. 2016. “Scene Classification via a Gradient Boosting Random Convolutional Network Framework.” IEEE Transactions on Geoscience and Remote Sensing 54 (3): 1–10. doi:10.1109/LGRS.2016.2519241.
- Zhang, W., S. Shan, W. Gao, X. Chen, and H. Zhang. 2005. “Local gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition.” In Tenth IEEE International Conference on Computer Vision, IEEE, 17-21 October, Beijing, China, 1: 786-791. DOI: 10.1109/ICCV.2005.147.