Supervised Classification of Very High Resolution Optical Images Using Wavelet-Based Textural Features

IEEE Transactions on Geoscience and Remote Sensing

Volumn 54, Issue 6, 2016, Pages 3722-3735

  Regniers, Olivier ^a,b   Bombrun, Lionel ^b,c   Lafon, Virginie ^b,d   Germain, Christian ^b,c  

^a UNIVERSITÉ DE BORDEAUX   (France)

^b I SEA Bordeaux Technowest   (France)

^c BORDEAUX SCIENCES AGRO   (France)

^d GEO Transfert ADERA   (France)

Author keywords

Classification; multivariate models; optical image; texture; very high resolution (VHR); wavelet

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONTENT BASED RETRIEVAL; GEOMETRICAL OPTICS; IMAGE CLASSIFICATION; OPTICAL RESOLVING POWER; SUPERVISED LEARNING; TEXTURES;

CONTENT BASED IMAGE RETRIEVAL; GRAY LEVEL CO-OCCURRENCE MATRIX; MULTIVARIATE MODELS; OPTICAL IMAGE; SUPERVISED CLASSIFICATION; VERY HIGH RESOLUTION; VERY HIGH RESOLUTION OPTICAL IMAGES; WAVELET;

IMAGE TEXTURE;

DATABASE; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE RESOLUTION; NUMERICAL MODEL; PANCHROMATIC IMAGE; SEGMENTATION; WAVELET ANALYSIS;

OSTREIDAE;

EID: 84977853388     PISSN: 01962892     EISSN: 15580644     Source Type: Journal    
DOI: 10.1109/TGRS.2016.2526078     Document Type: Article

References (58)

1. R. M. Haralick, K. Shanmugam, I. K. Dinstein, "Textural features for image classification," IEEE Trans. Syst., Man Cybern., vol. SMC-3, no. 6, pp. 610-621, Nov. 1973.
2. F. Pacifici, M. Chini, W. J. Emery, "A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification," Remote Sens. Environ., vol. 113, no. 6, pp. 1276-1292, Jun. 2009.
3. M. Pesaresi, A. Gerhardinger, F. Kayitakire, "A robust built-up area presence index by anisotropic rotation-invariant textural measure," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 1, no. 3, pp. 180-192, Sep. 2008.
4. C. A. Coburn and A. C. B. Roberts, "A multiscale texture analysis procedure for improved forest stand classification," Int. J. Remote Sens., vol. 25, no. 20, pp. 4287-4308, Oct. 2004.
5. S. E. Franklin, R. J. Hall, L.M.Moskal, A. J. Maudie,M. B. Lavigne, "Incorporating texture into classification of forest species composition from airborne multispectral images," Int. J. Remote Sens., vol. 21, no. 1, pp. 61-79, Jan. 2000.
6. B. Béguet, S. Boukir, D. Guyon, N. Chehata, "Modelling-based feature selection for classification of forest structure using very high resolution multispectral imagery," in Proc. IEEE Int. Conf. SMC, 2013, pp. 4294-4299.
7. I. Champion, C. Germain, J. P. Da Costa, A. Alborini, P. Dubois-Fernandez, "Retrieval of forest stand age from SAR image texture for varying distance and orientation values of the gray level co-occurrence matrix," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 1, pp. 5-9, Jan. 2014.
8. F. Kayitakire, C. Hamel, P. Defourny, "Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery," Remote Sens. Environ., vol. 102, no. 3/4, pp. 390-401, 2006.
9. T. A. Warner and K. Steinmaus, "Spatial classification of orchards and vineyards with high spatial resolution panchromatic imagery," Photogramm. Eng. Remote Sens., vol. 71, no. 2, pp. 179-187, Feb. 2005.
10. A. Balaguer, L. A. Ruiz, T. Hermosilla, J. A. Recio, "Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification," Comput. Geosci., vol. 36, no. 2, pp. 231-240, Feb. 2010.
11. S. Aksoy, H. G. Akcay, T. Wassenaar, "Automatic mapping of linear woody vegetation features in agricultural landscapes using very high resolution imagery," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 1, pp. 511-522, Jan. 2010.
12. G. Rabatel, C. Delenne, M. Deshayes, "A non-supervised approach using Gabor filters for vine-plot detection in aerial images," Comput. Electron. Agric., vol. 62, no. 2, pp. 159-168, Jul. 2008.
13. A. Lucieer and H. van der Werff, "Panchromatic wavelet texture features fused with multispectral bands for improved classification of highresolution satellite imagery," in Proc. IEEE IGARSS, 2007, pp. 5154-5157.
14. L. A. Ruiz, A. Fdez-Sarriá, J. A. Recio, "Texture feature extraction for classification of remote sensing data using wavelet decomposition: A comparative study," in Proc. Int. Arch. Photogramm., Remote Sens. Spatial Inf. Sci., 2004, vol. XXXV-B4, pp. 1109-1114.
15. Q. Jackson and D. A. Landgrebe, "Adaptive Bayesian contextual classification based on Markov random fields," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 11, pp. 2454-2463, Nov. 2002.
16. Y. Zhao, L. Zhang, P. Li, B. Huang, "Classification of high spatial resolution imagery using improved Gaussian Markov random-field-based texture features," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5, pp. 1458-1468, May 2007.
17. C. Vaduva, I. Gavat, M. Datcu, "Deep learning in very high resolution remote sensing image information mining communication concept," in Proc. 20th EUSIPCO, 2012, pp. 2506-2510.
18. A. Romero, C. Gatta, G. Camps-Valls, "Unsupervised deep feature extraction for remote sensing image classification," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 3, pp. 1349-1362, Mar. 2016.
19. M. N. Do and M. Vetterli, "Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance," IEEE Trans. Image Process., vol. 11, no. 2, pp. 146-158, Feb. 2002.
20. S. G. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674-693, Jul. 1989.
21. S. K. Choy and C. S. Tong, "Statistical wavelet subband characterization based on generalized gamma density and its application in texture retrieval," IEEE Trans. Image Process., vol. 19, no. 2, pp. 281-289, Feb. 2010.
22. G. Verdoolaege, S. De Backer, P. Scheunders, "Multiscale colour texture retrieval using the geodesic distance between multivariate generalized Gaussian models," in Proc. IEEE ICIP, 2008, pp. 169-172.
23. L. Bombrun, S. N. Anfinsen, O. Harant, "A complete coverage of log-cumulant space in terms of distributions for polarimetric SAR data," in Proc. Int. Workshop Sci. Appl. POLinSAR, 2011, p. 136.
24. R. Kwitt and A. Uhl, "A joint model of complex wavelet coefficients for texture retrieval," in Proc. IEEE ICIP, 2009, pp. 1877-1880.
25. R. Pickard et al., Vistex Database. Cambridge, MA, USA: MIT Press, 1995.
26. P. Brodatz, Textures: A Photographic Album for Artists and Designers, vol. 66. New York, NY, USA: Dover, 1966.
27. O. Regniers, L. Bombrun, D. Guyon, J. C. Samalens, C. Germain, "Wavelet-based texture features for the classification of age classes in a maritime pine forest," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 3, pp. 621-625, Mar. 2015.
28. G. Camps, D. Tuia, L. Gomez, S. Jiménez, J. Malo, "The statistics of remote sensing images," in Remote Sensing Image Processing. San Rafael, CA, USA: Morgan & Claypool, 2011, ch. 2.
29. I. W. Selesnick, R. G. Baraniuk, N. C. Kingsbury, "The dual-tree complex wavelet transform," IEEE Signal Process. Mag., vol. 22, no. 6, pp. 123-151, Nov. 2005.
30. A. Laine and J. Fan, "Texture classification by wavelet packet signatures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no. 11, pp. 1186-1191, Nov. 1993.
31. M. Unser, "Texture classification and segmentation usingwavelet frames," IEEE Trans. Image Process., vol. 4, no. 11, pp. 1549-1560, Nov. 1995.
32. E. P. Simoncelli and W. T. Freeman, "The steerable pyramid: A flexible architecture for multi-scale derivative computation," in Proc. IEEE ICIP, 1995, vol. 3, pp. 3444-3444.
33. F. Pascal, P. Forster, J. P. Ovarlez, P. Larzabal, "Performance analysis of covariance matrix estimates in impulsive noise," IEEE Trans. Signal Process., vol. 56, no. 6, pp. 2206-2217, Jun. 2008.
34. C. C. Freitas, A. C. Frery, A. H. Correia, "The polarimetric G distribution for SAR data analysis," Environmetrics, vol. 16, no. 1, pp. 13-31, Feb. 2005.
35. G. Vasile, J.-P. Ovarlez, F. Pascal, C. Tison, "Coherency matrix estimation of heterogeneous clutter in high-resolution polarimetric SAR images," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 4, pp. 1809-1826, Apr. 2010.
36. F. Pascal, Y. Chitour, J. P. Ovarlez, P. Forster, P. Larzabal, "Covariance structure maximum-likelihood estimates in compound Gaussian noise: Existence and algorithm analysis," IEEE Trans. Signal Process., vol. 56, no. 1, pp. 34-48, Jan. 2008.
37. Y. Stitou, N. E. Lasmar, Y. Berthoumieu, "Copulas based multivariate gamma modeling for texture classification," in Proc. IEEE ICASSP, 2009, pp. 1045-1048.
38. N. E. Lasmar and Y. Berthoumieu, "Gaussian copula multivariate modeling for texture image retrieval using wavelet transforms," IEEE Trans. Image Process., vol. 23, no. 5, pp. 2246-2261, May 2014.
39. A. Sklar, "Random variables, joint distribution functions, copulas," Kybernetika, vol. 9, no. 6, pp. 449-460, Jan. 1973.
40. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, R. Jain, "Content-based image retrieval at the end of the early years," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349-1380, Dec. 2000.
41. L. Bombrun, Y. Berthoumieu, N. E. Lasmar, G. Verdoolaege, "Multivariate texture retrieval using the geodesic distance between elliptically distributed random variables," in Proc. IEEE ICIP, 2011, pp. 3637-3640.
42. C. Lenglet, M. Rousson, R. Deriche, O. Faugeras, "Statistics on the manifold of multivariate normal distributions: Theory and application to diffusion tensor MRI processing," J. Math. Imag. Vis., vol. 25, no. 3, pp. 423-444, Oct. 2006.
43. H. Müller, W. Müller, D. McG. Squire, S. Marchand-Maillet, T. Pun, "Performance evaluation in content-based image retrieval: Overview and proposals," Pattern Recognit. Lett., vol. 22, no. 5, pp. 593-601, Apr. 2001.
44. J. Davis and M. Goadrich, "The relationship between precision-recall and ROC curves," in Proc. 23rd Int. Conf. Mach. Learn., 2006, pp. 233-240.
45. L. Gueguen, "Classifying compound structures in satellite images: A compressed representation for fast queries," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 4, pp. 1803-1818, Apr. 2015.
46. L. Fang, S. Li, X. Kang, J. A. Benediktsson, "Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4186-4201, Aug. 2015.
47. N. Vasconcelos, P. Ho, P. Moreno, "The Kullback-Leibler kernel as a framework for discriminant and localized representations for visual recognition," in Proc. ECCV, 2004, pp. 430-441.
48. S. Jayasumana, R. Hartley, M. Salzmann, H. Li, M. Harandi, "Kernel methods on the Riemannian manifold of symmetric positive definite matrices," in Proc. IEEE Conf. CVPR, 2013, pp. 73-80.
49. T. Wassenaar, J. M. Robbez-Masson, P. Andrieux, F. Baret, "Vineyard identification and description of spatial crop structure by per-field frequency analysis," Int. J. Remote Sens., vol. 23, no. 17, pp. 3311-3325, Jan. 2002.
50. T. Ranchin, B. Naert, M. Albuisson, G. Boyer, P. Astrand, "An automatic method for vine detection in airborne imagery using the wavelet transform and multiresolution analysis," Photogramm. Eng. Remote Sens., vol. 67, no. 1, pp. 91-98, Jan. 2001.
51. S. Aksoy, I. Z. Yalniz, K. Tasdemir, "Automatic detection and segmentation of orchards using very high resolution imagery," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 8, pp. 3117-3131, Aug. 2012.
52. R. M. Haralick, "Statistical and structural approaches to texture," Proc. IEEE, vol. 67, no. 5, pp. 786-804, May 1979.
53. Y. O. Ouma, R. Tateishi, J. T. Sri-Sumantyo, "Urban features recognition and extraction from very-high resolution multi-spectral satellite imagery: A micro-macro texture determination and integration framework," IET Image Process., vol. 4, no. 4, pp. 235-254, Aug. 2010.
54. D. Comaniciu and P.Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603-619, May 2002.
55. B. H. Choe, D. J. Kim, J. H. Hwang, Y. Oh, W. M. Moon, "Detection of oyster habitat in tidal flats using multi-frequency polarimetric SAR data," Estuarine, Coast. Shelf Sci., vol. 97, pp. 28-37, 2012.
56. M. Gade, S. Melchionna, K. Stelzer, J. Kohlus, "Multi-frequency SAR data help improving the monitoring of intertidal flats on the German north sea coast," Estuarine, Coast. Shelf Sci., vol. 140, pp. 32-42, Mar. 2014.
57. P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 7, pp. 629-639, Jul. 1990.
58. M. Fauvel, J. Chanussot, J. A. Benediktsson, "A spatial-spectral kernel-based approach for the classification of remote sensing images," Pattern Recognit., vol. 45, no. 1, pp. 381-392, Jan. 2012.