Remote sensing image scene classification using multi-scale completed local binary patterns and fisher vectors

Remote Sensing

Volumn 8, Issue 6, 2016, Pages

  Huang, Longhui ^a   Chen, Chen ^b   Li, Wei ^a   Du, Qian ^c  

^a BEIJING UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

^b The University of Texas at Dallas   (United States)

^c MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Completed local binary patterns; Extreme learning machine; Fisher vector; Multi scale analysis; Remote sensing image scene classification

Indexed keywords

BENCHMARKING; BINARY IMAGES; BINS; CLASSIFICATION (OF INFORMATION); CONTENT BASED RETRIEVAL; ENCODING (SYMBOLS); IMAGE RECONSTRUCTION; KNOWLEDGE ACQUISITION; LAND USE; LEARNING SYSTEMS; REMOTE SENSING; VECTORS;

EXTREME LEARNING MACHINE; FISHER VECTORS; LOCAL BINARY PATTERNS; MULTI SCALE ANALYSIS; REMOTE SENSING IMAGES;

IMAGE CLASSIFICATION;

EID: 84974807312     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8060483     Document Type: Article

References (40)

1. Yang, J.; Jiang, Y.-G.; Hauptmann, A.G.; Ngo, C.-W. Evaluating bag-of-visual-words representations in scene classification. In Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval, the 15th ACM International Conference on Multimedia, Augsburg, Bavaria, Germany, 23-28 September 2007; pp. 197-206
2. Lazebnik, S.; Schmid, C.; Ponce, J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, USA, 17-22 June 2006; pp. 2169-2178
3. Yang, Y.; Newsam, S. Spatial pyramid co-occurrence for image classification. In Proceedings of the International Conference on Computer Vision, Barcelona, Spain, 6-13 November 2011; pp. 1465-1472
4. Zhou, L.; Zhou, Z.; Hu, D. Scene classification using a multi-resolution bag-of-features model. Pattern Recognit. 2013, 46, 424-433
5. Zhao, L.-J.; Tang, P.; Huo, L.-Z. Land-use scene classification using a concentric circle-structured multiscale bag-of-visual-words model. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 4620-4631
6. Lowe, D.G. Distinctive image features from scale-invariant key points. Int. J. Comput. Vis. 2004, 60, 91-110
7. Risojevic, V.; Babic, Z. Aerial image classification using structural texture similarity. In Proceedings of the IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Bilbao, Spain, 14-17 December 2011; pp. 190-195
8. Risojević, V.; Momić, S.; Babić, Z. Gabor descriptors for aerial image classification. In Adaptive and Natural Computing Algorithms; Springer: Berlin, Germany; Heidelberg, Germany, 2011; pp. 51-60
9. Oliva, A.; Torralba, A. Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. J. Comput. Vis. 2001, 42, 145-175
10. Zheng, X.; Sun, X.; Fu, K.; Wang, H. Automatic annotation of satellite images via multifeature joint sparse coding with spatial relation constraint. IEEE Geosci. Remote Sens. Lett. 2013, 10, 652-656
11. Risojevic, V.; Babic, Z. Fusion of global and local descriptors for remote sensing image classification. IEEE Geosci. Remote Sens. Lett. 2013, 10, 836-840
12. Goodfellow, I.; Courville, A.; Bengio, Y. Deep Learning. Book in Preparation for MIT Press; The MIT Press: Cambridge, MA, USA, 2016
13. Bengio, Y. Learning deep architectures for AI. Found. Trends Mach. Learn. 2009, 2, 1-127
14. Yue, J.; Zhao, W.; Mao, S.; Liu, H. Spectral-Spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sens. Lett. 2015, 6, 468-477
15. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Image net classification with deep convolutional neural networks. In Neural Information Processing Systems; Neural Information Processing Systems Foundation, Inc.: San Diego, CA, USA, 2012; pp. 1106-1114
16. Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7-12 June 2015; pp. 1-9
17. Chen, C.; Zhang, B.; Su, H.; Li, W.; Wang, L. Land-use scene classification using multi-scale completed local binary patterns. Signal Image Video Process. 2015, 10, 1-8
18. Guo, Z.; Zhang, L.; Zhang, D. A Completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process. 2010, 19, 1657-1663
19. Perronnin, F.; Sánchez, J.; Mensink, T. Improving the Fisher Kernel for Large-Scale Image Classification. Computer Vision-ECCV 2010 Lecture Notes in Computer Science; Springer-Verlag: Berlin, Germany, 2010; pp. 143-156
20. Huang, G.-B.; Zhou, H.; Ding, X.; Zhang, R. Extreme Learning Machine for Regression and Multiclass Classification. IEEE Trans. Syst. Man Cybern. 2012, 42, 513-529
21. Ojala, T.; Pietikainen, M.; Maenpaa, T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24, 971-987
22. Li, W.; Chen, C.; Su, H.; Du, Q. Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans. Geosci. Remote Sens. 2015, 53, 3681-3693
23. Krapac, J.; Verbeek, J.; Jurie, F. Modeling spatial layout with fisher vectors for image categorization. In Proceedings of International Conference on Computer Vision, Barcelona, Spain, 6-13 November 2011; pp. 1487-1494
24. Sánchez, J.; Perronnin, F.; Mensink, T.; Verbeek, J. Image classification with the fisher vector: Theory and practice. Int. J. Comput. Vis. 2013, 105, 222-245
25. Liu, C. Maximum likelihood estimation from incomplete data via EM-type Algorithms. In Advanced Medical Statistics; World Scientific Publishing Co.: Hackensack, NJ, USA, 2003; pp. 1051-1071
26. Jaakkola, T.S.; Haussler, D. Exploiting generative models in discriminative classifiers. Adv. Neural Inf. Process. Syst. 1999, 11, 487-493
27. Perronnin, F.; Dance, C. Fisher kernels on visual vocabularies for image categorization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 17-22 June 2007; pp. 1-8
28. Yang, Y.; Newsam, S. 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, 3-5 November 2010; pp. 270-279
29. Dai, D.; Yang, W. Satellite image classification via two-layer sparse coding with biased image representation. IEEE Geosci. Remote Sens. Lett. 2011, 8, 173-176
30. Sheng, G.; Yang, W.; Xu, T.; Sun, H. High-resolution satellite scene classification using a sparse coding based multiple feature combination. Int. J. Remote Sens. 2012, 33, 2395-2412
31. Ren, J.; Zabalza, J.; Marshall, S.; Zheng, J. Effective feature extraction and data reduction in remote sensing using hyperspectral imaging [applications corner]. IEEE Sign. Process. Mag. 2014, 31, 149-154
32. Chen, C.; Li, W.; Tramel, E.W.; Fowler, J.E. Reconstruction of hyperspectral imagery from random projections using multi hypothesis prediction. IEEE Trans. Geosci. Remote Sens. 2014, 52, 365-374
33. Cheriyadat, A.M. Unsupervised feature learning for aerial scene classification. IEEE Trans. Geosci. Remote Sens. 2014, 52, 439-451
34. Zhang, F.; Du, B.; Zhang, L. Saliency-guided unsupervised feature learning for scene classification. IEEE Trans. Geosci. Remote Sens. 2015, 53, 2175-2184
35. Shao, W.; Yang, W.; Xia, G.-S.; Liu, G. A hierarchical scheme of multiple feature fusion for high-resolution satellite scene categorization. In Lecture Notes in Computer Science Computer Vision Systems; Springer: Berlin, Germany; Heidelberg, Germany, 2013; pp. 324-333
36. Chen, S.; Tian, Y. Pyramid of spatial relatons for scene-level land use classification. IEEE Trans. Geosci. Remote Sens. 2015, 53, 1947-1957
37. Cvetković, S.; Stojanović, M.B.; Nikolić, S.V. Multi-channel descriptors and ensemble of extreme learning machines for classification of remote sensing images. Sign. Process. 2015, 39, 111-120
38. Keiller, N.; Waner, O.; Jefersson, A.; Dos, S. Improving spatial feature representation from aerial scenes by using convolutional networks. In Proceedings of the SIBGRAPI Conference on Graphics, Patterns and Images, Salvador, 26-29 August 2015; pp. 44-51
39. Zhang, F.; Du, B.; Zhang, L. Scene classification via a gradient boosting random convolutional network framework. IEEE Trans. Geosci. Remote Sens. 2016, 54, 1793-1802
40. Keiller, N.; Otavio, P.; Jefersson, S. Towards better exploiting convolutional neural networks for remote sensing scene classification. 2016, ArXiv E-Prints, arXiv:1602.01517, http://arxiv.org/abs/1602.01517