Auto-encoder-based shared mid-level visual dictionary learning for scene classification using very high resolution remote sensing images

IET Computer Vision

Volumn 9, Issue 5, 2015, Pages 639-647

  Cheng, Gong ^a   Zhou, Peicheng ^a   Han, Junwei ^a   Guo, Lei ^a   Han, Jungong ^b  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b Civolution   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE CLASSIFICATION; LEARNING SYSTEMS; SEMANTICS; SIGNAL ENCODING;

COMPREHENSIVE EVALUATION; IMAGE REPRESENTATIONS; LOW-LEVEL IMAGE FEATURES; MACHINE LEARNING TECHNIQUES; REMOTE SENSING IMAGES; SEMANTIC INFORMATION; STATE-OF-THE-ART APPROACH; VERY HIGH RESOLUTION;

REMOTE SENSING;

EID: 84942017110     PISSN: 17519632     EISSN: 17519640     Source Type: Journal    
DOI: 10.1049/iet-cvi.2014.0270     Document Type: Article

References (43)

1. Cheng G. Guo L. Zhao T.: 'Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA', Int. J. Remote Sens., 2013, 34, (1), pp. 45-59
2. Yang Y. Newsam S.: 'Geographic image retrieval using local invariant features', IEEE Trans. Geosci. Remote Sens., 2013, 51, (2), pp. 818-832
3. Cheriyadat A.M.: 'Unsupervised feature learning for aerial scene classification', IEEE Trans. Geosci. Remote Sens., 2014, 52, (1), pp. 439-451
4. Yang Y. Newsam S.: 'Bag-of-visual-words and spatial extensions for land-use classification'. Proc. ACM SIGSPATIAL Int. Conf. Advances in Geographic Information Systems, San Jose, USA, 2010, pp. 270-279
5. Yang Y. Newsam S.: 'Spatial pyramid co-occurrence for image classification'. Proc. IEEE Int. Conf. on Computer Vision, Barcelona, Spain, 2011, pp. 1465-1472
6. Li H. Gu H. Han Y. Yang J.: 'Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine', Int. J. Remote Sens., 2010, 31, (6), pp. 1453-1470
7. Huang X. Zhang L. Wang L.: 'Evaluation of morphological texture features for mangrove forest mapping and species discrimination using multispectral IKONOS imagery', IEEE Geosci. Remote Sens. Lett., 2009, 6, (3), pp. 393-397
8. Xu S. Fang T. Li D. Wang S.: 'Object classification of aerial images with bag-of-visual words', IEEE Geosci. Remote Sens. Lett., 2010, 7, (2), pp. 366-370
9. Longbotham N. Chaapel C. Bleiler L.: 'Very high resolution multiangle urban classification analysis', IEEE Trans. Geosci. Remote Sens., 2012, 50, (4), pp. 1155-1170
10. Moustakidis S. Mallinis G. Koutsias N. Theocharis J.B. Petridis V.: 'SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images', IEEE Trans. Geosci. Remote Sens., 2012, 50, (1), pp. 149-169
11. Munoz-Mari J. Tuia D. Camps-Valls G.: 'Semisupervised classification of remote sensing images with active queries', IEEE Trans. Geosci. Remote Sens., 2012, 50, (10), pp. 3751-3763
12. Tuia D. Volpi M. Dalla Mura M. Rakotomamonjy A. Flamary R.: 'Automatic feature learning for spatio-spectral image classification with sparse SVM', IEEE Trans. Geosci. Remote Sens., 2014, 52, (10), pp. 6062-6074
13. Shao L. Liu L. Li X.: 'Feature learning for image classification via multiobjective genetic programming', IEEE Trans. Neural Netw. Learn. Syst., 2014, 25, (7), pp. 1359-1371
14. Zabalza J. Ren J. Marshall S. Wang J.: 'Singular spectrum analysis for effective feature extraction in hyperspectral imaging', IEEE Geosci. Remote Sens. Lett., 2014, 11, (11), pp. 1886-1890
15. Tang J. Shao L. Li X.: 'Efficient dictionary learning for visual categorization', Comput. Vis. Image Underst., 2014, 124, pp. 91-98
16. Ren J. Zabalza J. Marshall S. Zheng J.: 'Effective feature extraction and data reduction in remote sensing using hyperspectral imaging', IEEE Signal Process. Mag., 2014, 31, (4), pp. 149-154
17. Cheng G. Han J. Guo L.: 'Object detection in remote sensing imagery using a discriminatively trained mixture model', ISPRS J. Photogramm. Remote Sens., 2013, 85, pp. 32-43
18. Han J. Zhou P. Zhang D.: 'Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding', ISPRS J. Photogramm. Remote Sens., 2014, 89, pp. 37-48
19. Bhagavathy S. Manjunath B.S.: 'Modeling and detection of geospatial objects using texture motifs', IEEE Trans. Geosci. Remote Sens., 2006, 44, (12), pp. 3706-3715
20. Cheng G. Han J. Zhou P. Guo L.: 'Scalable multi-class geospatial object detection in high-spatial-resolution remote sensing images'. Proc. IEEE Int. Geoscience and Remote Sensing Symp., Quebec, Canada, 2014, pp. 2479-2482
21. Wen X. Shao L. Fang W. Xue Y.: 'Efficient feature selection and classification for vehicle detection', IEEE Trans. Circuits Syst. Video Technol., 2014, doi: 10.1109/TCSVT.2014.2358031
22. Cheng G. Han J. Zhou P. Guo L.: 'Multi-class geospatial object detection and geographic image classification based on collection of part detectors', ISPRS J. Photogramm. Remote Sens., 2014, 98, pp. 119-132
23. Fernando B. Fromont E. Tuytelaars T.: 'Mining mid-level features for image classification', Int. J. Comput. Vis., 2014, 108, (3), pp. 186-203
24. Li F.F. Perona P.: 'A Bayesian hierarchical model for learning natural scene categories'. Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, San Diego, USA, 2005, pp. 524-531
25. Lazebnik S. Schmid C. Ponce J.: 'Beyond bags of features: spatial pyramid matching for recognizing natural scene categories'. Proc. IEEE Int. Conf. Computer Vision Pattern Recognition, NY, USA, 2006, pp. 2169-2178
26. Lowe D.G.: 'Distinctive image features from scale-invariant keypoints', Int. J. Comput. Vis., 2004, 60, (2), pp. 91-110
27. Singh S. Gupta A. Efros A.A.: 'Unsupervised discovery of mid-level discriminative patches'. Proc. European Conf. Computer Vision, Firenze, Italy, 2012, pp. 73-86
28. Li Q. Wu J. Tu Z.: 'Harvesting mid-level visual concepts from large-scale internet images'. Proc. IEEE Int. Conf. Computer Vision Pattern Recognition, Portland, USA, 2013, pp. 851-858
29. Doersch C. Gupta A. Efros A.A.: 'Mid-level visual element discovery as discriminative mode seeking'. Proc. Conf. Advances Neural Information Processing Syst., NV, USA, 2013, pp. 494-502
30. Sun J. Ponce J.: 'Learning discriminative part detectors for image classification and cosegmentation'. Proc. IEEE Int. Conf. Computer Vision, Sydney, Australia, 2013, pp. 3400-3407
31. Juneja M. Vedaldi A. Jawahar C.V. Zisserman A.: 'Blocks that shout: distinctive parts for scene classification'. Proc. IEEE Int. Conf. Computer Vision Pattern Recognition, Portland, USA, 2013, pp. 923-930
32. Zhang L. Zhen X. Shao L.: 'Learning object-to-class kernels for scene classification' IEEE Trans. Image Process. 2014, 23, (8), pp. 3241-3253
33. Zhu F. Shao L.: 'Weakly-supervised cross-domain dictionary learning for visual recognition', Int. J. Comput. Vis., 2014, 109, (1-2), pp. 42-59
34. Song H.O. Zickler S. Althoff T.: 'Sparselet models for efficient multiclass object detection'. Proc. European Conf. Computer Vision, Firenze, Italy, 2012, pp. 802-815
35. Felzenszwalb P.F. Girshick R.B. McAllester D. Ramanan D.: 'Object detection with discriminatively trained part-based models', IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (9), pp. 1627-1645
36. Girshick R.B. Song H.O. Darrell T.: 'Discriminatively activated sparselets'. Proc. IEEE Int. Conf. Machine Learning, Atlanta, USA, 2013, pp. 196-204
37. Kokkinos I.: 'Shufflets: shared mid-level parts for fast object detection'. Proc. IEEE Int. Conf. Computer Vision, Sydney, Australia, 2013, pp. 1393-1400
38. Dalal N. Triggs B.: 'Histograms of oriented gradients for human detection'. Proc. IEEE Int. Conf. Computer Vision Pattern Recognition, San Diego, USA, 2005, pp. 886-893
39. Rumelhart D.E. Hinton G.E. Williams R.J.: 'Learning representations by back-propagating errors', Nature, 1986, 323, pp. 533-536
40. Hinton G.E. Salakhutdinov R.R.: 'Reducing the dimensionality of data with neural networks', Science, 2006, 313, (5786), pp. 504-507
41. Ng A.: 'CS294A lecture notes: sparse autoencoder' (Stanford University, Palo Alto, 2010)
42. Vincent P. Larochelle H. Bengio Y. Manzagol P.-A.: 'Extracting and composing robust features with denoising autoencoders'. Proc. Conf. Advances Neural Information Processing Systems, British Columbia, Canada, 2008, pp. 1096-1103
43. Nocedal J.: 'Updating quasi-Newton matrices with limited storage', Math. Comput., 1980, 35, (151), pp. 773-782