An analysis on deep learning approaches: addressing the challenges in remote sensing image retrieval

References

• Abadi, M., A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, and M. Devin, “Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” (2016a), arXiv:1603.04467.
   Google Scholar
• Al Rahhal, M. M., Y. Bazi, T. Abdullah, M. L. Mekhalf, and M. Zuair. 2020. “Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues.” MDPI, Applied Sciences 10: 8931. doi:https://doi.org/10.3390/app10248931.
   Google Scholar
• Alzu’bi, A., A. Amira, and N. Ramzan. 2019. “Learning Transfer Using Deep Convolutional Features for Remote Sensing Image Retrieval.” IAENG - International Journal of Computer Science 46: 4.
   Google Scholar
• Bei, Z., Y. Zhong, G.-S. Xia, and L. Zhang. 2016. “Dirichlet Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery.” IEEE Transactions on Geoscience Remote Sensing 54: 2108–2123. doi:https://doi.org/10.1109/TGRS.2015.2496185.
   Web of Science ®Google Scholar
• Boualleg, Y., and M. Farah, “Enhanced Interactive Remote Sensing Image Retrieval with Scene Classification Convolutional Neural Networks Model,” In Proceedings of the IGARSS 2018, IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, (2018), 4748–4751.
   Google Scholar
• Cao, R., Q. Zhang, J. Zhu, L. Qing, L. Qingquan, B. Liu, and G. Qiu. 2019. “Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network.” International Journal of Remote Sensing 41 (2): 740–751. doi:https://doi.org/10.1080/2150704X.2019.1647368.
   Web of Science ®Google Scholar
• Castelluccio, M., G. Poggi, C. Sansone, and L. Verdoliva, “Land Use Classification in Remote Sensing Images by Convolutional Neural Networks,” (2015), arXiv eprint:1508.00092.
   Google Scholar
• Chaudhuri, U., B. Banerjee, and A. Bhattacharya. 2019. “Siamese Graph Convolutional Network for Content-Based Remote Sensing Image Retrieval.” Computer Vision and Image Understanding 184: 22–30. doi:https://doi.org/10.1016/j.cviu.2019.04.004.
   Web of Science ®Google Scholar
• Chaudhuri, U., B. Banerjee, A. Bhattacharya, and M. Datcu. 2020a. “CMIR-NET: A Deep Learning Based Model for Cross-Modal Retrieval in Remote Sensing.” Pattern Recognition Letters 131: 456–462. doi:https://doi.org/10.1016/j.patrec.2020.02.006.
   Web of Science ®Google Scholar
• Chaudhuri, U., B. Banerjee, A. Bhattacharya, and M. Datcu. 2020b. “A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images.” IEEE Geoscience and Remote Sensing Letters. arXiv:2008.05225v1.
   Google Scholar
• Chaudhuri, U., B. Banerjee, A. Bhattacharya, and M. Datcu. 2020c. CrossATNet - A Novel Cross-Attention Based Framework for Sketch-based Image Retrieval. Image and Vision Computing.
   Google Scholar
• Chebbi, I., N. Mellouli, I. R. Farah, and M. Lamolle. 2021. “Big Remote Sensing Image Classification Based on Deep Learning Extraction Features and Distributed Spark Frameworks.” Big Data Cognitive Computing 5 (21): 21. doi:https://doi.org/10.3390/bdcc5020021.
   Google Scholar
• Chen, Y., L. Xiaoqiang, and S. Wang. 2020. “Deep Cross-Modal Image–Voice Retrieval in Remote Sensing.” IEEE Transactions on Geoscience and Remote Sensing 58 (10): 7049–7061. doi:https://doi.org/10.1109/TGRS.2020.2979273.
   Web of Science ®Google Scholar
• Chen, Y., and X. Lu. 2020. “A Deep Hashing Technique for Remote Sensing Image-Sound Retrieval.” MDPI, Remote Sensing 12 (1): 84. doi:https://doi.org/10.3390/rs12010084.
   Web of Science ®Google Scholar
• Cheng, G., J. Han, and X. Lu, “Remote Sensing Image Scene Classification: Benchmark and State-of-the-art,” Proceedings IEEE, (2017), 105, 1865–1883.
   Google Scholar
• Cheriyadat, A. M. 2014. “Unsupervised Feature Learning for Aerial Scene Classification.” IEEE Transaction on Geoscience Remote Sensing 52: 439–451. doi:https://doi.org/10.1109/TGRS.2013.2241444.
   Web of Science ®Google Scholar
• Compri, M., B. Demir, and N. X. Girói. 2017. “Multi-label Remote Sensing Image Retrieval Based on Deep Features.”
   Google Scholar
• Dean, J., and S. Ghemawat. 2010. “Mapreduce: A Flexible Data Processing Tool.” Communication ACM 53: 72–77. doi:https://doi.org/10.1145/1629175.1629198.
   Web of Science ®Google Scholar
• Demir, B., and L. Bruzzone. 2016. “Hashing-based Scalable Remote Sensing Image Search and Retrieval in Large Archives.” IEEE Transactions on Geoscience and Remote Sensing 54 (2): 892–904. doi:https://doi.org/10.1109/TGRS.2015.2469138.
   Web of Science ®Google Scholar
• Du, B., X. Li, D. Tao, and X. Lu, “Deep Semantic Understanding of High Resolution Remote Sensing Image,” In Proceedings of International Conference on Computer, Information and Telecommunication Systems, (2016).
   Google Scholar
• Famao, Y., H. Xiao, X. Zhao, M. Dong, W. Luo, and W. Min. 2018. “Remote Sensing Image Retrieval Using Convolutional Neural Network Features and Weighted Distance.” IEEE Geoscience and Remote Sensing Letters 15 (10): 1535–1539. doi:https://doi.org/10.1109/LGRS.2018.2847303.
   Web of Science ®Google Scholar
• Fan, H., X. Tong, G.-S. Xia, and L. Zhang, “Delving into Deep Representations for Remote Sensing Image Retrieval,” International Conference on Signal Processing Proceedings, ICSP, (2016), 198–203.
   Google Scholar
• Fan, L., H. Zhao, and H. Zhao. 2020. ““Distribution Consistency Loss for Large-Scale Remote Sensing Image Retrieval.” MDPI, Remote Sensing 12: 175. doi:https://doi.org/10.3390/rs12010175.
   Web of Science ®Google Scholar
• Fernandez-Beltran, R., B. Demir, F. Pla, and A. Plaza. 2021. “Unsupervised Remote Sensing Image Retrieval Using Probabilistic Latent Semantic Hashing.” IEEE Geoscience and Remote Sensing Letters 18 (2): 256–260. doi:https://doi.org/10.1109/LGRS.2020.2969491.
   Web of Science ®Google Scholar
• Fey, M., and J. E. Lenssen, “Fast Graph Representation Learning with PyTorch Geometric,” (2019), arXiv:1903.02428.
   Google Scholar
• Ge, Y., S. Jiang, Q. Xu, C. Jiang, and F. Ye. 2018. “Exploiting Representations from Pre-trained Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval.” Multimedia Tools and Applications 77: 17489–17515. doi:https://doi.org/10.1007/s11042-017-5314-5.
   Web of Science ®Google Scholar
• Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. “Generative Adversarial Nets.” In Advances in Neural Information Processing Systems, 2672–2680. Cambridge, MA, USA: MIT Press.
   Google Scholar
• Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. “Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. doi:https://doi.org/10.1016/j.rse.2017.06.031.
   Web of Science ®Google Scholar
• Guo, M., C. Zhou, and J. Liu. 2019. “Jointly Learning of Visual and Auditory: A New Approach for RS Image and Audio Cross-Modal Retrieval.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (11): 4644–4654. doi:https://doi.org/10.1109/JSTARS.2019.2949220.
   Web of Science ®Google Scholar
• Guo, W., J. Wang, and S. Wang. 2019. ““Deep Multimodal Representation Learning: A Survey.” IEEE Access 7: 63373–63394. doi:https://doi.org/10.1109/ACCESS.2019.2916887.
   Web of Science ®Google Scholar
• Han, L., L. Peng, X. Bai, C. Grecos, X. Zhang, and P. Ren. 2020. “Cohesion-Intensive Deep Hashing for Remote Sensing Image Retrieval.” MDPI, Remote Sensing 12: 101. doi:https://doi.org/10.3390/rs12010101.
   Web of Science ®Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, (2016), 770–778.
   Google Scholar
• Hinton, G. E., N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving Neural Networks by Preventing Co-adaptation of Feature Detectors,” (2012), arXiv:1207.0580.
   Google Scholar
• Hinton, G. E., S. Osindero, and Y. W. Teh. 2006. “A Fast Learning Algorithm For Deep Belief Nets.” Neural Computation 18: 1527–1554. doi:https://doi.org/10.1162/neco.2006.18.7.1527.
   PubMed Web of Science ®Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation, MIT Press 8 (8): 1735–1780. doi:https://doi.org/10.1162/neco.1997.9.8.1735.
   Google Scholar
• Hong, D., L. Gao, J. Yao, B. Zhang, A. Plaza, and J. Chanussot. 2020. “Graph Convolutional Networks for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing. doi:https://doi.org/10.1109/TGRS.2020.3015157.
   Web of Science ®Google Scholar
• Hong, D., N. Yokoya, J. Chanussot, and X. X. Zhu. 2019. “An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing.” IEEE Transactions on Image Processing 28 (4): 1923–1938. doi:https://doi.org/10.1109/TIP.2018.2878958.
   Web of Science ®Google Scholar
• Hou, D., Z. Miao, H. Xing, and W. Hao. 2020. “Exploiting Low Dimensional Features from the MobileNets for Remote Sensing Image Retrieval.” Journal of Earth Science Informatics 13: 1437–1443. doi:https://doi.org/10.1007/s12145-020-00484-3.
   Web of Science ®Google Scholar
• Hoxha, G., F. Melgani, and B. Demir. 2020. “Toward Remote Sensing Image Retrieval under a Deep Image Captioning Perspective,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.” 13: 4462–4475.
   Google Scholar
• Imbriaco, R., C. Sebastian, E. Bondarev, and H. N. D. W. Peter. 2019. “Aggregated Deep Local Features for Remote Sensing Image Retrieval.” MDPI, Remote Sensing 11: 5.
   Web of Science ®Google Scholar
• Ioffe, S., and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Co-variate Shift,” In Proceedings of the 32nd International Conference on Machine Learning, Lile, France, (2015), 1–9.
   Google Scholar
• Jia, Y., E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional Architecture for Fast Feature-Embedding,” In Proceedings of the 22nd ACM International Conference on Multimedia, (2014), 675–678.
   Google Scholar
• Jiang, T., G. Xia, Q. Lu, and W. Shen. 2017. “Retrieving Aerial Scene Images with Learned Deep Image-Sketch Features.” Journal of Computer Science and Technology 32 (4): 726–737. doi:https://doi.org/10.1007/s11390-017-1754-7.
   Web of Science ®Google Scholar
• Kim, J., and M. Chi. 2021. “SAFFNet: Self-Attention-Based Feature Fusion Network for Remote Sensing Few-Shot Scene Classification.” MDPI, Remote Sensing 13: 2532. doi:https://doi.org/10.3390/rs13132532.
   Web of Science ®Google Scholar
• Kingma, D. P., and M. Welling, “Auto-encoding Variational Bayes,” In Proceedings International Conference Learning Representations, 2014, 1( 1),1–14.
   Google Scholar
• Kong, J., Q. Sun, M. Mukherjee, and J. Lloret. 2020. “Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval.” MDPI, Remote Sensing 12 (7): 1164. doi:https://doi.org/10.3390/rs12071164.
   Web of Science ®Google Scholar
• Kreuziger, T., M. Ravanbakhsh, and B. Demir, “A Novel Triplet Sampling Method for Multi-label Remote Sensing Image Search and Retrieval,” arXiv:2105.03647v1, 2021.
   Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” Advanced Neural Information Process System 25: 1097–1105.
   Google Scholar
• Lampert, C. H., H. Nickisch, and S. Harmeling. 2014. “Attribute-based Classification for Zero-Shot Visual Object Categorization.” IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (3): 453–465. doi:https://doi.org/10.1109/TPAMI.2013.140.
   PubMed Web of Science ®Google Scholar
• LeCun, Y., B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. 1989. “Backpropagation Applied to Hand-written Zip Code Recognition.” Neural Computation 1 (4): 541–551. doi:https://doi.org/10.1162/neco.1989.1.4.541.
   Web of Science ®Google Scholar
• Li, Y., D. Kong, Y. Zhang, Y. Tan, and L. Chen. 2021. “Robust Deep Alignment Network with Remote Sensing Knowledge Graph for Zero-shot and Generalized Zero-shot Remote Sensing Image Scene Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 179: 145–158. doi:https://doi.org/10.1016/j.isprsjprs.2021.08.001.
   Web of Science ®Google Scholar
• Li, Y., Y. Zhang, X. Huang, H. Zhu, and J. Ma. 2020. “Large-scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks.” IEEE Transactions on Geoscience and Remote Sensing 56 (2): 950–965. doi:https://doi.org/10.1109/TGRS.2017.2756911.
   Web of Science ®Google Scholar
• Liu, C., J. Ma, X. Tang, X. Zhang, and L. Jiao, “Adversarial Hash-Code Learning for Remote Sensing Image Retrieval,” In IEEE International Geoscience and Remote Sensing Symposium, (2019), 4324–4327.
   Google Scholar
• Liu, P., G. Gou, X. Shan, D. Tao, and Q. Zhou. 2020a. “Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval.” MDPI, Sensors 20: 291. doi:https://doi.org/10.3390/s20010291.
   Web of Science ®Google Scholar
• Liu, Y., C. Chen, Z. Han, L. Ding, and Y. Liu. 2020b. “High-Resolution Remote Sensing Image Retrieval Based on Classification-Similarity Networks and Double Fusion.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13. doi:https://doi.org/10.1109/JSTARS.2020.2981372.
   Google Scholar
• Liu, Y., L. Ding, C. Chen, and Y. Liu. 2020c. “Similarity-Based Unsupervised Deep Transfer Learning for Remote Sensing Image Retrieval.” IEEE Transactions on Geoscience and Remote Sensing 1–18.
   Web of Science ®Google Scholar
• Long, M., Y. Cao, J. Wang, and M. Jordan, “Learning Transferable Features with Deep Adaptation Networks,” In Proceedings ICML, Lille, France, (2015), 97–105.
   Google Scholar
• Moustafa, M. S., S. Ahmed, and A. A. Hamed. 2020. “Learning to Hash with Convolutional Network for Multi-label Remote Sensing Image Retrieval.” International Journal of Intelligent Engineering and Systems 13 (5): 539–548. doi:https://doi.org/10.22266/ijies2020.1031.47.
   Google Scholar
• Napoletano, P. 2017. “Visual Descriptors for Content-based Retrieval of Remote-Sensing Images.” International Journal of Remote Sensing 39 (5): 1–34.
   Web of Science ®Google Scholar
• Peng, L., L. Han, X. Tao, and X. Zhang. 2020. “Hashing Nets for Hashing: A Quantized Deep Learning to Hash Framework for Remote Sensing Image Retrieval.” IEEE Transactions on Geoscience and Remote Sensing 58: 10.
   Web of Science ®Google Scholar
• Peng, L., X. Zhang, X. Zhu, and P. Ren. 2018. “Online Hashing for Scalable Remote Sensing Image Retrieval.” MDPI, Remote Sensing 10: 709. doi:https://doi.org/10.3390/rs10050709.
   Web of Science ®Google Scholar
• Ranzato, M., C. S. Poultney, S. Chopra, and Y. LeCun. 2007. “Efficient Learning of Sparse Representations with an Energy-based Model.” In Advances in Neural Information Processing Systems - 19 (NIPS’06), 1137–1144. MIT Press.
   Google Scholar
• Razavian, A. S., H. Azizpour, J. Sullivan, and S. Carlsson. 2014. “CNN Features Off-the-shelf: An Astounding Baseline for Recognition.” In CVPR Workshop, 512–519.
   Google Scholar
• Reato, T., B. Demir, and L. Bruzzone. 2019. “An Unsupervised Multicode Hashing Method for Accurate and Scalable Remote Sensing Image Retrieval.” IEEE Geoscience and Remote Sensing Letters 16 (2): 276–280. doi:https://doi.org/10.1109/LGRS.2018.2870686.
   Web of Science ®Google Scholar
• Richards, J. A., and X. Jia. 2006. Remote Sensing Digital Image Analysis: An Introduction. 4th Ed. ed. New York, USA: Springer.
   Google Scholar
• Roy, S., E. Sangineto, B. Demir, and N. Sebe. 2020. “Metric-Learning-Based Deep Hashing Network for Content-based Retrieval of Remote Sensing Images.” IEEE Geoscience and Remote Sensing Letters 1–5. doi:https://doi.org/10.1109/LGRS.2020.3013235.
   Google Scholar
• Schmitt, M., and X. X. Zhu. 2016. “Data Fusion and Remote Sensing - an Ever-growing Relationship.” IEEE Geoscience and Remote Sensing Magazine 4 (4): 6–23. doi:https://doi.org/10.1109/MGRS.2016.2561021.
   Web of Science ®Google Scholar
• Shao, Z., K. Yang, and W. Zhou. 2018. “Performance Evaluation of Single-label and Multi-label Remote Sensing Image Retrieval Using a Dense Labeling Dataset.” MDPI, Remote Sensing 10 (6): 964. doi:https://doi.org/10.3390/rs10060964.
   Web of Science ®Google Scholar
• Simonyan, K., and A. Zisserman, “Very Deep Convolutional Networks for Large-scale Image Recognition,” (2014), arXiv:1409.1556.
   Google Scholar
• Song, W., S. Li, and J. Benediktson, “Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images,” (2019), arXiv:1909.04614.
   Google Scholar
• Srivastava, N., G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov. 2014. “Dropout: A Simple Way to Prevent Neural Networks from Overfitting.” Journal of Machine Learning Research 15: 1929–1958.
   Web of Science ®Google Scholar
• Sudha, S. K., and S. Aji. 2019. “A Review on Recent Advances in Remote Sensing Image Retrieval Techniques.” Journal of the Indian Society of Remote Sensing 47 (12): 2129–2139. doi:https://doi.org/10.1007/s12524-019-01049-8.
   Web of Science ®Google Scholar
• Sumbul, G., M. Charfuelan, B. Demir, and V. Markl, “BigEarthNet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding,” In Proceedings of IGARSS, (2019).
   Google Scholar
• Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going Deeper with Convolutions,” In Proceedings of the IEEE CVPR Conference, (2015), 1–9.
   Google Scholar
• Tang, X., C. Liu, M. Jingjing, X. Zhang, F. Liu, and L. Jiao. 2019. “Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing.” MDPI, Remote Sensing 11 (17): 1–36.
   Web of Science ®Google Scholar
• Tang, X., L. Jiao, W. J. Emery, F. Liu, and D. Zhang. 2017. “Two-stage Reranking for Remote Sensing Image Retrieval.” IEEE Transactions on Geoscience Remote Sensing 55: 5798–5817. doi:https://doi.org/10.1109/TGRS.2017.2714676.
   Web of Science ®Google Scholar
• Tang, X., X. Zhang, F. Liu, and L. Jiao. 2018. “Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval.” MDPI, Remote Sensing 10 (8): 1–30.
   Web of Science ®Google Scholar
• Tong, X.-Y., G.-S. Xia, H. Fan, Y. Zhong, M. Datcu, and L. Zhang. 2019. “Exploiting Deep Features for Remote Sensing Image Retrieval - A Systematic Investigation.” IEEE Transactions on Big Data 6 (3): 507–521. doi:https://doi.org/10.1109/TBDATA.2019.2948924.
   Web of Science ®Google Scholar
• Valaboju, S., and M. Venkatesan. 2020. “An Efficient Image Retrieval System for Remote Sensing Images Using Deep Hashing Network.” In Emerging Research in Data Engineering Systems and Computer Communications, Advances in Intelligent Systems and Computing, edited by P. Venkata Krishna and M. Obaidat, 1054. Singapore: Springer.
   Google Scholar
• Wang, Y., L. Zhang, D. Hao, J. Lu, H. Huang, Z. Liang, J. Liu, T. Hong, and X. Xing. 2017. “Learning a Discriminative Distance Metric with Label Consistency for Scene Classification.” IEEE Transactions on Geoscience Remote Sensing 55: 4427–4440. doi:https://doi.org/10.1109/TGRS.2017.2692280.
   Web of Science ®Google Scholar
• Xia, G.-S., W. Yang, J. Delon, Y. Gousseau, H. Sun, and H. Maître, “Structural High-resolution Satellite Image Indexing,” In Proceedings of the ISPRS TC VII Symposium — 100 Years ISPRS, Vienna, Austria, (2010), 298–303.
   Google Scholar
• Xiong, W., L. Yafei, Y. Cui, X. Zhang, and G. Xiangqi. 2019. ““A Discriminative Feature Learning Approach for Remote Sensing Image Retrieval,” MDPI.” Remote Sensing 11: 281. doi:https://doi.org/10.3390/rs11030281.
   Web of Science ®Google Scholar
• Xiong, W., Y. Lv, X. Zhang, and Y. Cui. 2020a. “Learning to Translate for Cross-source Remote Sensing Image Retrieval.” IEEE Transactions on Geoscience and Remote Sensing 58: 7. doi:https://doi.org/10.1109/TGRS.2020.2968096.
   Web of Science ®Google Scholar
• Xiong, W., Z. Xiong, Y. Cui, and Y. Lv. 2020b. “A Discriminative Distillation Network for Cross-Source Remote Sensing Image Retrieval.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 1234–1247. doi:https://doi.org/10.1109/JSTARS.2020.2980870.
   Web of Science ®Google Scholar
• Xiong, W., Z. Xiong, Y. Zhang, Y. Cui, and G. Xiangqi. 2020c. “A Deep Cross-Modality Hashing Network for SAR and Optical Remote Sensing Images Retrieval.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13.
   Web of Science ®Google Scholar
• Xu, F., W. Yang, T. Jiang, S. Lin, H. Luo, and G. Xia. 2020. Mental Retrieval of Remote Sensing Images via Adversarial Sketch-Image Feature Learning. IEEE Transactions on Geoscience and Remote Sensing.
   Google Scholar
• Yang, Y., and S. Newsam, “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, San Jose, CA, USA, (2010), 270–279.
   Google Scholar
• Yansheng, L., M. Jiayi, and Y. Zhang. 2020. “Image Retrieval from Remote Sensing Big Data: A Survey.” Information Fusion 67: 94–115.
   Web of Science ®Google Scholar
• Yansheng, L., Y. Zhang, C. Tao, and H. Zhu. 2016. “Content Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion.” MDPI, Remote Sensing 8 (9): 709. doi:https://doi.org/10.3390/rs8090709.
   Web of Science ®Google Scholar
• Yansheng, L., Y. Zhang, X. Huang, and M. Jiayi. 2018. “Learning Source-invariant Deep Hashing Convolutional Neural Networks for Cross-Source Remote Sensing Image Retrieval.” IEEE Transactions on Geoscience and Remote Sensing 56 (11): 6521–6536. doi:https://doi.org/10.1109/TGRS.2018.2839705.
   Web of Science ®Google Scholar
• Yansheng, L., Z. Zhu, Y. Jin-Gang, and Y. Zhang. 2020. “Learning Deep Cross-modal Embedding Networks for Zero-shot Remote Sensing Image Scene Classification.” IEEE Transactions on Geoscience and Remote Sensing. doi:https://doi.org/10.1109/TGRS.2020.3047447.
   Google Scholar
• Yessou, H., G. Sumbul, and B. Um Demir, “A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification,” arXiv:2009.13935v1, (2020).
   Google Scholar
• Yoshua, B., P. Lamblin, D. Popovici, and H. Larochelle. 2007. “Greedy Layer-wise Training of Deep Network.” In Advances in Neural Information Processing Systems - 19 (NIPS 2006), 153–160. Cambridge, MA, USA: MIT Press.
   Google Scholar
• Yuan, Z., W. Zhang, K. Fu, X. Li, C. Deng, H. Wang, and X. Sun. 2021. “Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval.” IEEE Transactions on Geoscience and Remote Sensing 1–19. doi:https://doi.org/10.1109/TGRS.2021.3078451.
   Google Scholar
• Yun, M.-S., W.-J. Nam, and S.-W. Lee. 2020. “Coarse-to-Fine Deep Metric Learning for Remote Sensing Image Retrieval.” MDPI, Remote Sensing 12 (219): 1–20.
   Google Scholar
• Zeiler, M. D., and R. Fergus. 2014. “Visualizing and Understanding Convolutional Networks.” In Computer Vision–ECCV, Springer, 818–833.
   Google Scholar
• Zhang, L., L. Zhang, and B. Du. 2016. “Deep Learning for Remote Sensing Data: A Technical Tutorial on the State-of-the-art.” IEEE Geoscience and Remote Sensing Magazine 4 (2): 22–40. doi:https://doi.org/10.1109/MGRS.2016.2540798.
   Web of Science ®Google Scholar
• Zhang, M., Q. Cheng, F. Luo, and L. Ye. 2021b. “A Triplet Non-local Neural Network with Dual-anchor Triplet Loss for High-resolution Remote Sensing Image Retrieval.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (14): 2711–2723. doi:https://doi.org/10.1109/JSTARS.2021.3058691.
   Google Scholar
• Zhang, Y., X. Min, and L. Xiaodong, “Remote Sensing Image Retrieval Based on DenseNet Model and CBAM,” IEEE 3rd International Conference on Computer and Communication Engineering Technology, CCET, (2020), 86–90.
   Google Scholar
• Zhao, H., L. Yuan, H. Zhao, and Z. Wang. 2021. “Global-Aware Ranking Deep Metric Learning for Remote Sensing Image Retrieval.” IEEE Geoscience and Remote Sensing Letters. doi:https://doi.org/10.1109/LGRS.2021.3059908.
   Google Scholar
• Zhi-Ze, W., C. Zou, Y. Wang, M. Tan, and T. Weise. 2021. “Rotation-Aware Representation Learning for Remote Sensing Image Retrieval.” Information Sciences 572: 404–423. doi:https://doi.org/10.1016/j.ins.2021.04.078.
   Web of Science ®Google Scholar
• Zhou, W., S. Newsam, C. Li, and Z. Shao. 2018. “PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval.” ISPRS Journal of Photogrammetry and Remote Sensing 145: 197–209. doi:https://doi.org/10.1016/j.isprsjprs.2018.01.004.
   Web of Science ®Google Scholar
• Zhou, W., S. Newsam, L. Congmin, and Z. Shao. 2017. “Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval.” MDPI, Remote Sensing 9 (5): 489. doi:https://doi.org/10.3390/rs9050489.
   Web of Science ®Google Scholar
• Zhou, W., X. Deng, and Z. Shao. 2018. “Region Convolutional Features for Multi-label Remote Sensing Image Retrieval.” Computer Vision and Pattern Recognition, arXiv:1807.08634, 1–8.
   Google Scholar
• Zhou, W., Z. Shao, C. Diao, and Q. Cheng. 2015. “High-Resolution Remote-Sensing Imagery Retrieval Using Sparse Features by Auto-Encoder.” Remote Sensing Letters 6 (10): 775–783. doi:https://doi.org/10.1080/2150704X.2015.1074756.
   Web of Science ®Google Scholar
• Zhu, X. X., D. Tuia, L. Mou, G.-S. Xia, L. Zhang, X. Feng, and F. Fraundorfer, “Deep Learning in Remote Sensing - A Review,” IEEE Geoscience and Remote Sensing Magazine, (2017), arXiv:1710.0395v1.
   Google Scholar
• Zhuo, Z., and Z. Zhou. 2020. “Low Dimensional Discriminative Representation of Fully Connected Layer Features Using Extended LargeVis Method for High-Resolution Remote Sensing Image Retrieval.” MDPI, Sensors (Basel) 20 (17): 4718. doi:https://doi.org/10.3390/s20174718.
   Web of Science ®Google Scholar
• Zhuo, Z., and Z. Zhou. 2021. “Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network.” MDPI, Remote Sensing 13: 869. doi:https://doi.org/10.3390/rs13050869.
   Google Scholar
• Zou, Q., L. Ni, T. Zhang, and Q. Wang. 2015. “Deep Learning Based Feature Selection for Remote Sensing Scene Classification.” IEEE Geoscience and Remote Sensing Letters 12 (11): 2321–2325. doi:https://doi.org/10.1109/LGRS.2015.2475299.
   Web of Science ®Google Scholar