A Review on Recent Advances in Remote Sensing Image Retrieval Techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development
Link
http://link.springer.com/content/pdf/10.1007/s12524-019-01049-8.pdf
Reference46 articles.
1. Aptoula, E. (2014). Remote sensing image retrieval with global morphological texture descriptors. IEEE Transactions on Geoscience and Remote Sensing,52(5), 3023–3034.
2. Blanchart, P., Ferecatu, M., Cui, S., & Datcu, M. (2014). Pattern retrieval in large image databases using multiscale coarse-to-fine cascaded active learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,7(4), 1127–1141.
3. Chaudhuri, B., Demir, B., Bruzzone, L., & Chaudhuri, S. (2016). Region-based retrieval of remote sensing images using an unsupervised graph-theoretic approach. IEEE Geoscience and Remote Sensing Letters,13(7), 987–991.
4. Dai, O. E., Demir, B., Sankur, B., & Bruzzone, L. (2018). A novel system for content-based retrieval of single and multi-label high-dimensional remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,11(7), 2473–2490.
5. Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008). Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys,40(2), 1–60.
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