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A Review on Recent Advances in Remote Sensing Image Retrieval Techniques

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Abstract

Due to the rapid advancements in the remote sensing (RS) imaging modalities, the scientific fraternity has been challenged to develop sophisticated methods for retrieving similar images from huge image archives. Developing efficient retrieval methods has become more challenging, as the quantity of RS image databases is growing fast in the spatial information domain. Even though numerous techniques have been developed for Remote Sensing Image Retrieval (RSIR) in the last decade, most of them are found to be less effective on a large volume of RS image databases. Several studies have been conducted to analyze the issues related to the challenges involved in the design of efficient and reliable retrieval techniques for an RSIR. A systematic study has been conducted on the existing RSIR methods, especially on the performance of the techniques with large datasets, and the findings are explained in this paper. The discussions and findings presented in this paper will give new insight, into the different RSIR techniques. The recommendations given at the end of the paper will help the new researchers in the RS domain to choose effective methodologies that can improve the performance of the RSIR system in different retrieval schemes.

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References

  • Aptoula, E. (2014). Remote sensing image retrieval with global morphological texture descriptors. IEEE Transactions on Geoscience and Remote Sensing,52(5), 3023–3034.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Demir, B., & Bruzzone, L. (2014). A novel active learning method in relevance feedback for content-based remote sensing image retrieval. IEEE Transactions on Geoscience and Remote Sensing,53(5), 2323–2334.

    Article  Google Scholar 

  • Demir, B., & Bruzzone, L. (2015). Hashing-based scalable remote sensing image search and retrieval in large archives. IEEE Transactions on Geoscience and Remote Sensing,54(2), 892–904.

    Article  Google Scholar 

  • Duan, J., Ma, C., Liu, S. B., & Zhang, J. (2013). The remote sensing image retrieval based on multi-feature. In Proceedings of SPIE image and signal processing for remote sensing XIX, Vol. 8892.

  • Ferecatu, M., & Boujemaa, N. (2007). Interactive remote-sensing image retrieval using active relevance feedback. IEEE Transactions on Geoscience and Remote Sensing,45(4), 818–826.

    Article  Google Scholar 

  • Grivei, A. C., Radoi, A., Vaduva, C., & Datcu, M. (2016). An active-learning approach to the query by example retrieval in remote sensing images. In International conference on communications (COMM) (pp. 377–380).

  • Hu, F., Tong, X., Xia, G. S., & Zhang, L. (2017). Delving into deep representations for remote sensing image retrieval. In International conference on signal processing proceedings, ICSP (pp. 198–203)

  • Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia (pp. 675–678). ACM

  • Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012) Imagenet Classification with Deep Convolutional Neural Networks. In NIPS’12 proceedings of the 25th international conference on neural information processing systems (pp. 1097–1105)

  • Laban, N., ElSaban, M., Nasr, A., & Onsi, H. (2012). System refinement for content based satellite image retrieval. The Egyptian Journal of Remote Sensing and Space Sciences,15(1), 91–97.

    Article  Google Scholar 

  • Li, Y., & Bretschneider, T. (2006). Remote sensing image retrieval using a context-sensitive bayesian network with relevance feedback. In IEEE international symposium on geoscience and remote sensing.

  • Li, J., & Narayanan, R. M. (2004). Integrated information mining and image retrieval in remote sensing. IEEE Transactions on Geoscience and Remote Sensing,42(3), 673–685.

    Article  Google Scholar 

  • Li, P., & Ren, P. (2017). Partial randomness hashing for large-scale remote sensing image retrieval. IEEE Geoscience and Remote Sensing Letters,14(3), 464–468.

    Article  Google Scholar 

  • Li, Y., Zhang, Y., Tao, C., & Zhu, H. (2016). Content-based high-resolution remote sensing image retrieval via unsupervised feature learning and collaborative affinity metric fusion. Remote Sensing, 8(9), 709.

    Article  Google Scholar 

  • Li, Y., Zhang, Y., Huang, X., Zhu, H., & Ma, J. (2018a). Large-scale remote sensing image retrieval by deep hashing neural networks. IEEE Transactions on Geoscience and Remote Sensing,56(2), 950–965.

    Article  Google Scholar 

  • Li, P., Zhang, X., Zhu, X., & Ren, P. (2018b). Online hashing for scalable remote sensing image retrieval. Remote Sensing,10(5), 709.

    Article  Google Scholar 

  • Jiao, L., Tang, X., Hou, B., & Wang, S. (2015). SAR images retrieval based on semantic classification and region-based similarity measure for earth observation. IEEE Selected Topics in Applied Earth Observations and Remote Sensing,8(8), 3876–3891.

    Article  Google Scholar 

  • Ma, C., Dai, Q., Liu, J., Liu, S., & Yang, J. (2014). An improved SVM model for relevance feedback in remote sensing image retrieval. International Journal of Digital Earth,7(9), 725–745.

    Article  Google Scholar 

  • Mitra, P., Shankar, B. U., & Pal, S. K. (2004). Segmentation of multispectral remote sensing images using active SVMs. Pattern Recognition Letters,25(9), 1067–1074.

    Article  Google Scholar 

  • Napoletano, P. (2017). Visual descriptors for content-based retrieval of remote-sensing images. International Journal of Remote Sensing,39(5), 1–34.

    Google Scholar 

  • Pang, S., Xue, J., Gao, Z., & Tian, Q. (2015). Image re-ranking with an alternating optimization. Neurocomputing,165, 423–432.

    Article  Google Scholar 

  • Peijun, D. U., Yunhao, C., Hong, T., & Tao, F. (2005). Study on content-based remote sensing image retrieval. In Proceedings, IEEE international geoscience, and remote sensing symposium, IGARSS ‘05

  • Rui, Y., Huang, T. S., Ortega, M., & Mehrotra, S. (1998). Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology,8(5), 644–655.

    Article  Google Scholar 

  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks,61, 85–117.

    Article  Google Scholar 

  • Shao, Z., Yang, K., & Zhou, W. (2018). A benchmark dataset for performance evaluation of multi-label remote sensing image retrieval, MDPI. Remote Sensing,10(8), 964.

    Article  Google Scholar 

  • Song, Q., Huang, R., & Wang, K. (2015). Remote sensing image retrieval based on attribute profiles. In International conference on computer science and mechanical automation, (pp. 231–234).

  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabonovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9)

  • Tang, X., & Jiao, L. (2017). Fusion similarity-based reranking for SAR image retrieval. IEEE Geoscience and Remote Sensing Letters,14(2), 242–246.

    Article  Google Scholar 

  • Tang, X., Jiao, L., Emery, W. J., Liu, F., & Zhang, D. (2017). Two-stage re-ranking for remote sensing image retrieval. IEEE Transactions on Geoscience and Remote Sensing,55(10), 5798–5817.

    Article  Google Scholar 

  • Tuia, D., Volpi, M., Copa, L., Kanevski, M., & Munoz, J. (2011). A survey of active learning algorithms for supervised remote sensing image classification. IEEE Journal of Selected Topics in Signal Processing,5(3), 606–617.

    Article  Google Scholar 

  • Wang, M., & Song, T. (2013). Remote sensing image retrieval by scene semantic matching. IEEE Transactions on Geoscience and Remote Sensing,51(5), 2874–2886.

    Article  Google Scholar 

  • Wang, Y., Zhang, L., Tong, X., Zhang, L., Zhang, Z., Liu, H., et al. (2016). A three-layered graph-based learning approach for remote sensing image retrieval. IEEE Transactions on Geoscience and Remote Sensing,54(10), 6020–6034.

    Article  Google Scholar 

  • Xia, G. S., Tong, X. Y., Hu, F., Zhong, Y., Datcu, M., Zhang, L. (2017). Exploiting deep features for remote sensing image retrieval: A systematic investigation. arXiv:1707.07321, 2 July 2017

  • Yang, Y., & Newsam, S. (2010). Bag-of-visual-word and spatial extensions for land use classification. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information system (pp. 270–279). ACM

  • Yang, Y., & Newsam, S. (2013). Geographic image retrieval using local invariant features. IEEE Transactions on Geoscience and Remote Sensing,51(2), 818–832.

    Article  Google Scholar 

  • Ye, D., Li, Y., Tao, C., Xie, X., & Wang, X. (2017). Multiple feature hashing learning for large-scale remote sensing image retrieval. ISPRS International Journal of Geo-Information,6(11), 364.

    Article  Google Scholar 

  • Ye, F., Xiao, H., Zhao, X., Dong, M., Luo, W., & Min, W. (2018). Remote sensing image retrieval using convolutional neural network features and weighted distance. IEEE Geoscience and Remote Sensing Letters,15(2), 232–236.

    Article  Google Scholar 

  • Yue, J., Zhao, W., Mao, S., & Liu, H. (2015). Spectral-spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sensing Letters,6(6), 468–477.

    Article  Google Scholar 

  • Zhang, J., Geng, W., Liang, X., Li, J., Zhuo, L., & Zhou, Q. (2017a). Hyperspectral remote sensing image retrieval system using spectral and texture features. Applied Optics,56(16), 4785–4796.

    Article  Google Scholar 

  • Zhang, J., Zhou, Q., Zhuo, L., Geng, W., & Wang, S. (2017b). A CBIR system for hyperspectral remote sensing images using endmember extraction. International Journal of Pattern Recognition and Artificial Intelligence,31(4), 1752001.

    Article  Google Scholar 

  • Zhou, W., Newsam, S., Li, C., & Shao, Z. (2017). Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval. Remote Sensing,9(5), 489.

    Article  Google Scholar 

  • Zhou, W., Shao, Z., Diao, C., & Cheng, Q. (2015). High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder. Remote Sensing Letters,6(10), 775–783.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram, Kerala, 695581, India

    S. K. Sudha & S. Aji

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  1. S. K. Sudha
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  2. S. Aji
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Correspondence to S. K. Sudha.

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Sudha, S.K., Aji, S. A Review on Recent Advances in Remote Sensing Image Retrieval Techniques. J Indian Soc Remote Sens 47, 2129–2139 (2019). https://doi.org/10.1007/s12524-019-01049-8

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