Semantic annotation in earth observation based on active learning

References

• Blanchart, P. and Datcu, M., 2010. A semi-supervised algorithm for auto-annotation and unknown structures discovery in satellite image databases. IEEE Journal of Selected Topics Applied Earth Observations Remote Sensing, 3 (4), 698–717.
   Web of Science ®Google Scholar
• Breiman, L., 2001. Random forests. Machine Learning, 45 (1), 5–32.
   Web of Science ®Google Scholar
• Chen, J., et al., 2010. WLD: a robust local image descriptor. IEEE Transaction on Pattern Analysis Machine Intelligence, 32 (9), 1705–1720.
   PubMed Web of Science ®Google Scholar
• Costache, M., Maitre, H., and Datcu, M., 2006. Categorization based relevance feedback search engine for earth observation images repositories. In: Proceedings of IEEE international geoscience and remote sensing symposium (IGARSS). Denver, CO: IEEE, 13–16.
   Google Scholar
• Crawford, M., Tuia, D., and Yang, H., 2013. Active learning: any value for classification of remotely sensed data? Proceedings of the IEEE, 101 (3), 593–608.
   Web of Science ®Google Scholar
• Cui, S., Dumitru, C.O., and Datcu, M., 2013, September. Ratio-detector-based feature extraction for very high resolution SAR image patch indexing. IEEE Geoscience Remote Sensing Letters, 10 (5), 1175–1179.
   Web of Science ®Google Scholar
• Datcu, M. and Seidel, K., 2005, March. Human-centered concepts for exploration and understanding of earth observation images. IEEE Transaction on Geoscience Remote Sensing, 43 (3), 601–609.
   Web of Science ®Google Scholar
• Demir, B., Bovolo, F., and Bruzzone, L., 2012. Detection of land-cover transitions in multitemporal remote sensing images with active-learning-based compound classification. IEEE Transaction Geoscience Remote Sensing, 50 (5), 1930–1941.
   Web of Science ®Google Scholar
• Dumitru, C.O. and Datcu, M., 2013a. How many categories are in very high resolution SAR images? In: Proceedings of IEEE international geoscience and remote sensing symposium (IGARSS). Melbourne: IEEE, 1793–1796.
   Google Scholar
• Dumitru, C.O. and Datcu, M., 2013b. Information content of very high resolution SAR images: study of feature extraction and imaging parameters. IEEE Transaction on Geoscience Remote Sensing, 51 (8), 1–22.
   Web of Science ®Google Scholar
• Faur, D., et al., 2013. Multi-temporal analysis of floods and tsunami effects: annotation and quantitative analysis. In Multitemp 2013, 25–27 June Banff.
   Google Scholar
• Ferecatu, M. and Boujemaa, N., 2007. Interactive remote-sensing image retrieval using active relevance feedback. IEEE Transactions on Geoscience Remote Sensing, 45 (4), 818–826.
   Web of Science ®Google Scholar
• Lowe, D.G., 2004, November. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60 (2), 91–110.
   Web of Science ®Google Scholar
• Ma, C., et al., 2013. An improved SVM model for relevance feedback in remote sensing image retrieval. International Journal of Digital Earth, 0 (0), 1–21.
   Google Scholar
• Maji, S., Berg, A.C., and Malik, J., 2013, January. Efficient classification for additive kernel SVMs. IEEE Transactions on Pattern Analysis Machine Intelligence, 35 (1), 66–77.
   PubMed Web of Science ®Google Scholar
• Manjunath, B.S. and Ma, W.Y., 1996. Texture features for browsing and retrieval of image data. IEEE Transaction on Pattern Analysis Machine Intelligence, 18 (8), 837–842.
   Web of Science ®Google Scholar
• Popescu, A.A., Gavat, I., and Datcu, M., 2012. Contextual descriptors for scene classes in very high resolution SAR images. IEEE Geoscience Remote Sensing Letters, 9 (1), 80–84.
   Web of Science ®Google Scholar
• Settles, B., 2009. Active learning literature survey. Madison: University of Wisconsin, Computer Sciences Technical Report 1648.
   Google Scholar
• Shyu, C.-R., et al., 2007. GeoIRIS: Geospatial information retrieval and indexing system | content mining, semantics modeling, and complex queries. IEEE Transactions on Geoscience Remote Sensing, 45 (4), 839–852.
   PubMed Web of Science ®Google Scholar
• Singh, J. and Datcu, M. 2013. SAR image categorization with log cumulants of the fractional Fourier transform coefficients. IEEE Transactions on Geoscience Remote Sensing, 1–10. doi:10.1109/TGRS.2012.2230892.
   Google Scholar
• Tong, S., 2001. Active learning: theory and applications [online]. Thesis (PhD). Stanford University. Available from: http://www.robotics.stanford.edu/~stong/research.html [Accessed 8 November 2013].
   Google Scholar
• Tong, S. and Koller, D., 2002, March. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2 (22), 45–66.
   Google Scholar
• Tuia, D., Muñoz Marí, J., and Camps-Valls, G. 2012. Remote sensing image segmentation by active queries. Pattern Recognition, 45 (6), 2180–2192.
   Web of Science ®Google Scholar
• Tuia, D., et al., 2009. Active learning methods for remote sensing image classification. IEEE Transaction on Geoscience Remote Sensing, 47 (7), 2218–2232.
   Web of Science ®Google Scholar
• Tuia, D., et al., 2011. A survey of active learning algorithms for supervised remote sensing image classification. IEEE Journal of Selected Topics in Signal Process, 5, 606–617.
   Web of Science ®Google Scholar
• van Gemert, et al., 2010. Visual word ambiguity. IEEE Transactions on Pattern Analysis Machine Intelligence, 32 (7), 1271–1283.
   PubMed Web of Science ®Google Scholar
• Yu, L., He, Z., and Cao, Q. 2010, January. Gabor texture representation method for face recognition using the gamma and generalized Gaussian models. Image Vision Computer, 28 (1), 177–187.
   Web of Science ®Google Scholar