Multilabel Remote Sensing Image Retrieval Based on Fully Convolutional Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 13, Issue , 2020, Pages 318-328

  Shao, Zhenfeng ^a   Zhou, Weixun ^b   Deng, Xueqing ^c   Zhang, Maoding ^a   Cheng, Qimin ^d  

^a WUHAN UNIVERSITY   (China)

^b NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^c Merced   (United States)

^d HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Fully convolutional networks (FCN); multilabel retrieval; multilabel vector; region convolutional features (RCFs); remote sensing image retrieval (RSIR); single label retrieval

Indexed keywords

CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; IMAGE RETRIEVAL; IMAGE SEGMENTATION; REMOTE SENSING; SEMANTICS;

CONVOLUTIONAL NETWORKS; MULTI-LABEL; REGION CONVOLUTIONAL FEATURES (RCFS); REMOTE SENSING IMAGE RETRIEVAL; SINGLE-LABEL RETRIEVAL;

SEARCH ENGINES;

ARTIFICIAL NEURAL NETWORK; IMAGE ANALYSIS; MAPPING; PATTERN RECOGNITION; REMOTE SENSING; SEGMENTATION; VECTOR;

EID: 85080959722     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2019.2961634     Document Type: Article

References (36)

1. P. Bosilj, E. Aptoula, S. Lefèvre, and E. Kijak, "Retrieval of remote sensing images with pattern spectra descriptors, " ISPRS Int. J. Geo-Inf., vol. 5, no. 12, pp. 228-243, 2016.
2. E. Aptoula, "Remote sensing image retrieval with global morphological texture descriptors, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 3023-3034, May 2014.
3. Z. Shao, W. Zhou, L. Zhang, and J. Hou, "Improved color texture descrip-tors for remote sensing image retrieval, " J. Appl. Remote Sens., vol. 8, no. 1, 2014, Art. no. 083584.
4. J. Sivic and A. Zisserman, "Video Google: A text retrieval approach to object matching in videos, " in Proc. 9th IEEE Int. Conf. Comput. Vis., 2003, pp. 1470-1477.
5. B. Chaudhuri, B. Demir, L. Bruzzone, and S. Chaudhuri, "Region-based retrieval of remote sensing images using an unsupervised graph-theoretic approach, " IEEE Geosci. Remote Sens. Lett., vol. 13, no. 7, pp. 987-991, Jul. 2016.
6. Y. Yang and S. Newsam, "Geographic image retrieval using local invariant features, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 2, pp. 818-832, Feb. 2013.
7. V. Ranjan, N. Rasiwasia, and C. V. Jawahar, "Multi-label cross-modal retrieval, " in Proc. IEEE Int. Conf. Comput. Vis., 2015, pp. 4094-4102.
8. R. Li, Y. Zhang, Z. Lu, J. Lu, and Y. Tian, "Technique of image retrieval based on multi-label image annotation, " in Proc. 2nd Int. Conf. Multimedia Inf. Technol., 2010, pp. 10-13.
9. G. Nasierding and A. Z. Kouzani, "Empirical study of multi-label classification methods for image annotation and retrieval, " in Proc. Int. Conf. Digit. Image Comput. Techn. Appl., 2010, pp. 617-622.
10. M. R. Boutell, J. Luo, X. Shen, and C. M. Brown, "Learning multi-label scene classification, " Pattern Recognit., vol. 37, no. 9, pp. 1757-1771, 2004.
11. F. Li, Q. Dai, W. Xu, and G. Er, "Multilabel neighborhood propagation for region-based image retrieval, " IEEE Trans. Multimed., vol. 10, no. 8, pp. 1592-1604, Dec. 2008.
12. F. Zhao, Y. Huang, L. Wang, and T. Tan, "Deep semantic ranking based hashing for multi-label image retrieval, " in Proc. IEEE Conf. Comput. Vision Pattern Recognit. (CVPR), 2015, pp. 1556-1564.
13. M. Wang and T. Song, "Remote sensing image retrieval by scene semantic matching, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 5, pp. 2874-2886, May 2013.
14. M. Wang, Q. M. Wan, L. B. Gu, and T. Y. Song, "Remote-sensing image retrieval by combining image visual and semantic features, " Int. J. Remote Sens., vol. 34, no. 12, pp. 4200-4223, 2013.
15. O. E. Dai, B. Demir, B. Sankur, and L. Bruzzone, "A novel system for content based retrieval of multi-label remote sensing images, " in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2017, pp. 1744-1747.
16. O. E. Dai, B. Demir, B. Sankur, and L. Bruzzone, "A novel system for content-based retrieval of single and multi-label high-dimensional remote sensing images, " IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens., vol. 11, no. 7, pp. 2473-2490, Jul. 2018.
17. B. Chaudhuri, B. Demir, S. Chaudhuri, and L. Bruzzone, "Multilabel remote sensing image retrieval using a semisupervised graph-theoretic method, " IEEE Trans. Geosci. Remote Sens., vol. 56, no. 2, pp. 1144-1158, Feb. 2018.
18. L. Zhou, Z. Wang, Y. Luo, and Z. Xiong, "Separability and compactness network for image recognition and superresolution, " IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 11, pp. 3275-3286, Nov. 2019.
19. A. Sellami, M. Farah, I.R. Farah, and B. Solaiman, "Hyperspectralimagery classification based on semi-supervised 3-D deep neural network and adaptive band selection, " Expert Syst. Appl., vol. 129, pp. 246-259, 2019.
20. Y. Li, H. Zhang, and Q. Shen, "Spectral-spatial classification of hyper-spectral imagery with 3D convolutional neural network, " Remote Sens., vol. 9, no. 1, pp. 67-87, 2017.
21. Y. Li, Y. Zhang, X. Huang, H. Zhu, and J. Ma, "Large-scale remote sensing image retrieval by deep hashing neural networks, " IEEE Trans. Geosci. Remote Sens., vol. 56, no. 2, pp. 950-965, Feb. 2018.
22. W. Zhou, S. Newsam, C. Li, and Z. Shao, "Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval, " Remote Sens., vol. 9, no. 5, pp. 489-508, 2017.
23. P. Li, X. Zhang, X. Zhu, and P. Ren, "Online hashing for scalable remote sensing image retrieval, " Remote Sens., vol. 10, no. 5, pp. 709-753, 2018.
24. W. Zhou, S. Newsam, C. Li, and Z. Shao, "Patternnet:A benchmark dataset for performance evaluation of remote sensing image retrieval, " ISPRS J. Photogramm. Remote Sens., vol. 145, pp. 197-209, 2018.
25. P. Napoletano, "Visual descriptors for content-based retrieval of remote-sensing images, " Int. J. Remote Sens., vol. 39, no. 5, pp. 1343-1376, 2018.
26. Y. Li, Y. Zhang, X. Huang, and J. Ma, "Learning source-invariant deep hashing convolutional neural networks for cross-source remote sensing image retrieval, " IEEE Trans. Geosci. Remote Sens., vol. 56, no. 11, pp. 6521-6536, Nov. 2018.
27. J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2015, pp. 3431-3440.
28. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and F.-F. Li, "ImageNet: A large-scale hierarchical image database, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 2-9.
29. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition, " 2014, arXiv: 1409.1556.
30. W. Shao, Z. Yang, and K. Zhou, "Performance evaluation of single-label and multi-label remote sensing image retrieval using a dense labeling dataset, " Remote Sens., vol. 10, no. 6, pp. 964-976, 2018.
31. A. Vedaldi and K. Lenc, "MatConvNet: Convolutional neural networks for MATLAB, " in Proc. 23rd ACM Int. Conf. Multimedia, 2015, pp. 689-692.
32. T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971-987, Jul. 2002.
33. C. Liu and H. Wechsler, "Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition, " IEEE Trans. Image Process., vol. 11, no. 4, pp. 467-476, Apr. 2002.
34. A. Oliva and A. Torralba, "Modeling the shape of the scene: A holistic representation of the spatial envelope, " Int. J. Comput. Vis., vol. 42, no. 3, pp. 145-175, 2001.
35. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, "Return of the devil in the details: Delving deep into convolutional nets, " in Proc. Brit. Mach. Vis. Conf., 2014, pp. 1-11.
36. Y. Jia et al., "Caffe: Convolutional architecture for fast feature embedding, " in Proc. ACM Int. Conf. Multimedia, 2014, pp. 675-6781.