Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery

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

Volumn 10, Issue 9, 2017, Pages 4104-4115

  Wang, Guoli ^a,b   Fan, Bin ^a   Xiang, Shiming ^a   Pan, Chunhong ^a  

^a INSTITUTE OF AUTOMATION   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Convolutional neural networks (CNNs); mixed resolution representation; remote sensing scene classification; vector of locally aggregated descriptors (VLAD)

Indexed keywords

CONVOLUTION; ENCODING (SYMBOLS); IMAGE CLASSIFICATION; IMAGE CODING; IMAGE PROCESSING; IMAGE RECONSTRUCTION; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; SEMANTICS; SIGNAL ENCODING;

COMPUTATIONAL CONSUMPTION; CONVOLUTIONAL NEURAL NETWORK; DISCRIMINATIVE FEATURES; EXPERIMENTAL EVALUATION; HIERARCHICAL INFORMATION; REMOTE SENSING IMAGE PROCESSING; SCALE INVARIANT FEATURE TRANSFORMS; VECTOR OF LOCALLY AGGREGATED DESCRIPTORS;

REMOTE SENSING;

ARTIFICIAL NEURAL NETWORK; DATA SET; HIERARCHICAL SYSTEM; IMAGE CLASSIFICATION; IMAGE PROCESSING; REMOTE SENSING; SATELLITE IMAGERY; VECTOR;

EID: 85020074308     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2017.2705419     Document Type: Article

References (50)

1. Y. Yang and S. Newsam, "Bag-of-visual-words and spatial extensions for land-use classification, " in Proc. ACM Geograph. Inf. Syst., 2010, pp. 270-279.
2. D. G. Lowe, "Distinctive image features from scale-invariant keypoints, " Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
3. A. M. Cheriyadat, "Unsupervised feature learning for aerial scene classification, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 1, pp. 439-451, Jan. 2014.
4. M. L. Mekhalfi, F. Melgani, Y. Bazi, and N. Alajlan, "Land-use classification with compressive sensing multifeature fusion, " IEEE Geosci. Remote Sens. Lett., vol. 12, no. 10, pp. 2155-2159, Oct. 2015.
5. B. Zhao, Y. Zhong, L. Zhang, and B. Huang, "The fisher kernel coding framework for high spatial resolution scene classification, " Remote Sens., vol. 8, no. 2, p. 157, 2016.
6. R. Negrel, D. Picard, and P.-H. Gosselin, "Evaluation of second-order visual features for land-use classification, " in Proc. IEEE 12th Int. Workshop Content-Based Multimedia Indexing., 2014, pp. 1-5.
7. E. Li, P. Du, A. Samat, Y. Meng, andM. Che, "Mid-level feature representation via sparse autoencoder for remotely sensed scene classification, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 10, no. 3, pp. 1068-1081, Mar. 2017.
8. J. Chen, L. Jiao, and Z. Wen, "High-level feature selection with dictionary learning for unsupervised sar imagery terrain classification, " J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 10, no. 1, pp. 145-160, Jan. 2017.
9. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks, " in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1097-1105.
10. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition, " CoRR, vol. Abs/1409. 1556, 2014.
11. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition, " 2015. ArXiv:1512. 03385.
12. Z. Yan et al., "Hd-cnn: hierarchical deep convolutional neural networks for large scale visual recognition, " in Proc. IEEE Int. Conf. Comput. Vis., 2015, pp. 2740-2748.
13. M. Castelluccio, G. Poggi, C. Sansone, and L. Verdoliva, "Land use classification in remote sensing images by convolutional neural networks, " 2015. ArXiv:1508. 00092.
14. Y. Jia et al., "Caffe: Convolutional architecture for fast feature embedding, " in Proc. 22nd ACM Int. Conf. Multimedia, 2014, pp. 675-678.
15. C. Szegedy et al., "Going deeper with convolutions, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2015, pp. 1-9.
16. I. Sevo andA. Avramovíc, "Convolutional neural network based automatic object detection on aerial images, " IEEE Geosci. Remote Sens. Soc. Lett., vol. 13, no. 5, pp. 740-744, May 2016.
17. G. Cheng, P. Zhou, and J. Han, "Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 12, pp. 7405-7415, Dec. 2016.
18. K. Nogueira, O. A. Penatti, and J. A. dos Santos, "Towards better exploiting convolutional neural networks for remote sensing scene classification, " Pattern Recognit., vol. 61, pp. 539-556, 2017.
19. H. Jégou, M. Douze, C. Schmid, and P. Pérez, "Aggregating local descriptors into a compact image representation, " in Proc. IEEE Comput. Vis. Pattern Recognit., 2010, pp. 3304-3311.
20. Z. Zhou, Y. Wang, Q. J. Wu, C.-N. Yang, and X. Sun, "Effective and efficient global context verification for image copy detection, " IEEE Trans. Inf. Forensics Security, vol. 12, no. 1, pp. 48-63, Jan. 2017.
21. F. Hu, G.-S. Xia, Z. Wang, X. Huang, L. Zhang, andH. Sun, "Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 5, pp. 2015-2030, May 2015.
22. Q. Zhu, Y. Zhong, B. Zhao, G.-S. Xia, and L. Zhang, "Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery, " IEEE Geosci. Remote Sens. Lett., vol. 13, no. 6, pp. 747-751, Jun. 2016.
23. F. Zhang, B. Du, and L. Zhang, "Scene classification via a gradient boosting random convolutional network framework, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 3, pp. 1793-1802, Mar. 2016.
24. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition, " IEEE Proc., vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
25. S. Zha, F. Luisier, W. Andrews, N. Srivastava, and R. Salakhutdinov, "Exploiting image-trained CNN architectures for unconstrained video classification, " 2015. ArXiv:1503. 04144.
26. S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks, " in Proc. Adv. Neural Inf. Process. Syst., 2015, pp. 91-99.
27. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. Reed, "SSD: Single shot multibox detector, " 2015. ArXiv:1512. 02325.
28. K. He, X. Zhang, S. Ren, and J. Sun, "Spatial pyramid pooling in deep convolutional networks for visual recognition, " in Eur. Conf. Comput. Vis., Berlin, Germany: Springer, 2014, pp. 346-361.
29. M. Lin, Q. Chen, and S. Yan, "Network in network, " 2013. ArXiv:1312. 4400.
30. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, "Overfeat: Integrated recognition, localization and detection using convolutional networks, " 2013. ArXiv:1312. 6229.
31. M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks, " in Proc. Eur. Conf. Comput. Vis., Berlin, Germany: Springer, 2014, pp. 818-833.
32. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, "Return of the devil in the details: Delving deep into convolutional nets, " 2014. ArXiv:1405. 3531.
33. W. Zhao and S. Du, "Learning multiscale and deep representations for classifying remotely sensed imagery, " ISPRS J. Photogrammetry Remote Sens., vol. 113, pp. 155-165, 2016.
34. O. A. Penatti, K. Nogueira, and J. A. dos Santos, "Do deep features generalize from everyday objects to remote sensing and aerial scenes domains" in Proc. IEEE Comput. Vis. Pattern Recognit. Workshops, 2015, pp. 44-51.
35. P. Napoletano, "Visual descriptors for content-based retrieval of remote sensing images, " 2016. ArXiv:1602. 00970.
36. Y. Gong, L. Wang, R. Guo, and S. Lazebnik, "Multi-scale orderless pooling of deep convolutional activation features, " in Eur. Conf. Comput. Vis., Berlin, Germany: Springer, 2014, pp. 392-407.
37. Y. Cheng, R. Cai, X. Zhao, and K. Huang, "Convolutional fisher kernels for RGB-D object recognition, " in Proc. IEEE Int. Conf. 3D Vis., 2015, pp. 135-143.
38. D. Yoo, S. Park, J.-Y. Lee, and I. So Kweon, "Multi-scale pyramid pooling for deep convolutional representation, " in Proc. IEEE Comput. Vis. Pattern Recognit. Workshops, 2015, pp. 71-80.
39. T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, " IEEE Trans. Pattern Analysis Mach. Intell., vol. 24, no. 7, pp. 971-987, Jul. 2002.
40. F. Hu, G.-S. Xia, J. Hu, and L. Zhang, "Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery, " Remote Sens., vol. 7, no. 11, pp. 14 680-14 707, 2015.
41. B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, "Hypercolumns for object segmentation and fine-grained localization, " in Proc. IEEE Comput. Vis. Pattern Recognit., 2015, pp. 447-456.
42. J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation, " in Proc. IEEE Comput. Vis. Pattern Recognit., 2015, pp. 3431-3440.
43. X. Wang and E. Grimson, "Spatial latent Dirichlet allocation, " in Proc. Adv. Neural Inf. Process. Syst., 2008, pp. 1577-1584.
44. S. Chen and Y. Tian, "Pyramid of spatial relations for scene-level land use classification, " IEEE Trans. Geosci. Remote Sens., vol. 53, no. 4, pp. 1947-1957, Apr. 2015.
45. O. Russakovsky et al., "Imagenet large scale visual recognition challenge, " Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, 2015.
46. A. Vedaldi and K. Lenc, "Matconvnet: Convolutional neural networks for MATLAB, " in Proc. 23rd ACMInt. Conf. Multimedia., 2015, pp. 689-692.
47. V. Nair and G. E. Hinton, "Rectified linear units improve restricted Boltzmann machines, " in Proc. 27th Int. Conf. Mach. Learn., 2010, pp. 807-814.
48. A. Vedaldi and B. Fulkerson, "VLFeat: An open and portable library of computer vision algorithms, " in Proc. ACM Multimedia, 2010, pp. 1469-1472.
49. R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin, "LIBLINEAR:Alibrary for large linear classification, " J. Mach. Learn. Res., vol. 9, pp. 1871-1874, 2008.
50. F. Luus, B. Salmon, F. Van Den Bergh, and B. Maharaj, "Multiview deep learning for land-use classification, " IEEE Geosci. Remote Sens. Lett., vol. 12, no. 12, pp. 2448-2452, Dec. 2015.