Domain adaptation for the classification of remote sensing data: An overview of recent advances

IEEE Geoscience and Remote Sensing Magazine

Volumn 4, Issue 2, 2016, Pages 41-57

  Tuia, Devis ^a   Persello, Claudio ^b   Bruzzone, Lorenzo ^c  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b UNIVERSITY OF TWENTE   (Netherlands)

^c UNIVERSITY OF TRENTO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); PHOTOMAPPING;

ADAPTATION METHODS; CLASSIFICATION ALGORITHM; DOMAIN ADAPTATION; GEOGRAPHICAL AREA; REMOTE SENSING DATA; REMOTE-SENSING; REMOTELY SENSED IMAGES; REPRESENTATIVITY; SHORT TIME INTERVALS; SUPERVISED CLASSIFICATION;

REMOTE SENSING;

ADAPTIVE MANAGEMENT; DATA SET; GUIDELINE; IMAGE ANALYSIS; REMOTE SENSING; SATELLITE IMAGERY; SPATIAL RESOLUTION; SUPERVISED CLASSIFICATION;

EID: 84976403023     PISSN: 24732397     EISSN: 21686831     Source Type: Journal    
DOI: 10.1109/MGRS.2016.2548504     Document Type: Review

References (79)

1. R. De Jong, S. De Bruin, A. De Wit, M. E. Schaepman, and D. L. Dent, "Analysis of monotonic greening and browning trends from global NDVI time-series, " Remote Sens. Environ., vol. 115, no. 2, pp. 692-702, 2011
2. F. Pacifici, N. Longbotham, and W. Emery, "The importance of physical quantities for the analysis of multitemporal and multiangular optical very high spatial resolution images, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 10, pp. 6241-6256, 2014
3. J. J. Walker, K. M. De Beurs, R. H. Wynne, and F. Gao, "Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology, " Remote Sens. Environ., vol. 117, pp. 381-393, Feb. 2012
4. W. Liao, X. Huang, F. Van Collie, A. Gautama, W. Philips, H. Liu, T. Zhu, M. Shimoni, G. Moser, and D. Tuia, "Processing of thermal hyperspectral and digital color cameras: Outcome of the 2014 data fusion contest, " IEEE J. Sel. Topics Appl. Earth Observ., vol. 8, no. 6, pp. 2984-2996, 2015
5. J. Amorós-López, L. Gómez-Chova, L. Alonso, L. Guanter, R. Zurita-Milla, J. Moreno, and G. Camps-Valls, "Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring, " Int. J. Appl. Earth Obs. Geoinfo., vol. 23, pp. 132-141, Aug. 2013
6. I. Walde, S. Hese, C. Berger, and C. Schmullius, "From land cover-graphs to urban structure types, " Int. J. Geogr. Info. Sci., vol. 28, no. 3, pp. 584-609, 2014
7. L. Gómez-Chova, D. Tuia, G. Moser, and G. Camps-Valls, "Multimodal classification of remote sensing images: A review and future directions, " Proc. IEEE, vol. 103, no. 9, pp. 1560-1584, 2015
8. J. Verrelst, L. Alonso, J. P. R. Caicedo, J. Moreno, and G. Camps-Valls, "Gaussian Process retrieval of chlorophyll content from imaging spectroscopy data, " IEEE J. Sel. Topics Appl. Earth Observ., vol. 6, no. 2, pp. 867-874, 2013
9. J. Behmann, J. Steinrücken, and L. Plümer, "Detection of early plant stress responses in hyperspectral images, " ISPRS J. Int. Soc. Photo. Remote Sens., vol. 93, pp. 98-111, July 2014
10. M. Dalponte, L. Bruzzone, and D. Gianelle, "Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data, " Remote Sens. Environ., vol. 123, pp. 258-270, Aug. 2012
11. M. D. Fleming, J. S. Berkebile, and R. M. Hoffer, "Computer-aided analysis of LANDSAT-I MSS data: A comparison of three approaches, including a "modified clustering" approach, " Purdue University, West Lafayette, IN, Lab. Applicat. Remote Sensing Inform. Note 072475, 1975
12. I. Olthof, C. Butson, and R. Fraser, "Signature extension through space for northern landcover classification: A comparison of radiometric correction methods, " Remote Sens. Environ., vol. 95, no. 3, pp. 290-302, 2005
13. J. Quiñonero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, Eds., Dataset Shift in Machine Learning, Cambridge, MA: MIT Press, 2008
14. S. J. Pan and Q. Yang, "A survey on transfer learning, " IEEE Trans. Know. Data Eng., vol. 22, no. 10, pp. 1345-1359, 2010
15. K. Saenko, B. Kulis, M. Fritz, and T. Darrell, "Adapting visual category models to new domains, " in Proc. 11th Europ. Conf. Comput. Vision, Heraklion, Greece, 2010, pp. 213-226
16. V. M. Patel, R. Gopalan, R. Li, and R. Chellappa, "Visual domain adaptation: A survey of recent advances, " IEEE Signal Processing Mag., vol. 32, no. 3, pp. 53-69, 2015
17. M. Sugiyama and M. Kawanabe, Machine Learning in Non-stationary Environments, Cambridge, MA: MIT Press, 2012
18. G. Matasci, N. Longbotham, F. Pacifici, M. Kanevski, and D. Tuia, "Understanding angular effects in VHR imagery and their significance for urban land-cover model portability: A study of two multi-angle in-track image sequences, " ISPRS J. Int. Soc. Photo. Remote Sens., vol. 107, pp. 99-111, Sept. 2015
19. D. Tuia, E. Pasolli, and W. J. Emery, "Using active learning to adapt remote sensing image classifiers, " Remote Sens. Environ., vol. 115, no. 9, pp. 2232-2242, 2011
20. K. Bahirat, F. Bovolo, L. Bruzzone, and S. Chaudhuri, "A novel domain adaptation Bayesian classifier for updating landcover maps with class differences in source and target domains, " IEEE Trans. Geosci. Remote Sens., vol. 50, no. 7, pp. 2810-2826, 2012
21. B. Demir, F. Bovolo, and L. Bruzzone, "Updating land-cover maps by classification of image time series: A novel changedetection-driven transfer learning approach, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 300-312, 2013
22. G. Jun and J. Ghosh, "Semisupervised learning of hyperspectral data with unknown land-cover classes, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 273-282, 2013
23. J. J. Heckman, "Sample selection bias as a specification error, " Econometrica, vol. 47, no. 1, pp. 153-161, 1979
24. B. Zadrozny, "Learning and evaluating classifiers under sample selection bias, " in Proc. 21st Int. Conf. on Machine Learn., Banff, Canada, 2004, p. 114
25. J. Huang, A. Gretton, B. Schölkopf, A. J. Smola, and K. M. Borgwardt, "Correcting sample selection bias by unlabeled data, " in Proc. 21st Annu. Conf. Neural Inform. Proc. Syst.. Vancouver, Canada, 2007, pp. 601-608
26. C. Persello and L. Bruzzone, "Active and semisupervised learning for the classification of remote sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 11, pp. 6937-6956, 2014
27. C. Persello and L. Bruzzone, "Active learning for domain adaptation in the supervised classification of remote sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 50, no. 11, pp. 4468-4483, 2012
28. C. Persello, "Interactive domain adaptation for the classification of remote sensing images using active learning, " IEEE Geosci. Remote Sens. Lett., vol. 10, no. 4, pp. 736-740, 2013
29. E. Izquierdo-Verdiguier, V. Laparra, L. Gómez-Chova, and G. Camps-Valls, "Encoding invariances in remote sensing image classification with SVM, " IEEE Geosci. Remote Sens. Lett., vol. 10, no. 5, pp. 981-985, 2013
30. L. Bruzzone and C. Persello, "A novel approach to the selection of spatially invariant features for the classification of hyperspectral images with improved generalization capability, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 9, pp. 3180-3191, 2009
31. C. Persello and L. Bruzzone, "Kernel-based domain invariant feature selection in hyperspectral images for transfer learning, " IEEE Trans. Geosci. Remote Sens, vol. 54, no. 5, pp. 2615-2626, 2016
32. J. Ham, Y. Chen, M. M. Crawford, and J. Ghosh, "Investigation of the random forest framework for classification of hyperspectral data, " IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 492-501, 2005
33. S. Inamdar, F. Bovolo, L. Bruzzone, and S. Chaudhuri, "Multidimensional probability density function matching for preprocessing of multitemporal remote sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 4, pp. 1243-1252, 2008
34. A. Nielsen and M. J. Canty, "Kernel principal component and maximum autocorrelation factor analyses for change detection, " Proc. SPIE, vol. 7477, 74770T, 2009
35. G. Matasci, M. Volpi, M. Kanevski, L. Bruzzone, and D. Tuia, "Semisupervised transfer component analysis for domain adaptation in remote sensing image classification, " IEEE Trans. Geosci. Remote Sens., vol. 53, no. 7, pp. 3550-3564, 2015
36. A. A. Nielsen, "The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data, " IEEE Trans. Image Proc., vol. 16, no. 2, pp. 463-478, 2007
37. M. Volpi, G. Camps-Valls, and D. Tuia, "Spectral alignment of cross-sensor images with automated kernel canonical correlation analysis, " ISPRS J. Int. Soc. Photo. Remote Sens., vol. 107, pp. 50-63, Sept. 2015
38. D. Tuia, M. Volpi, M. Trolliet, and G. Camps-Valls, "Semisupervised manifold alignment of multimodal remote sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 12, pp. 7708-7720, 2014
39. D. Tuia and G. Camps-Valls, "Kernel manifold alignment for domain adaptation, " PLoS One, vol. 11, p. e0148655, Feb. 2016
40. D. Marcos Gonzalez, G. Camps-Valls, and D. Tuia, "Weakly supervised alignment of multisensor images, " in Proc. IEEE Int. Geosci. and Remote Sens. Symp (IGARSS), Milan, Italy, 2015, pp. 2588-2591
41. J. Montoya-Zegarra, C. Leistner, and K. Schindler, "Semantic tie points, " in Proc. IEEE Workshop Applic. Comput. Vision (WACV), Clearwater Beach, FL, 2013, pp. 377-384
42. H. L. Yang and M. M. Crawford, "Spectral and spatial proximitybased manifold alignment for multitemporal hyperspectral image classification, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 1, pp. 51-64, 2016
43. H. L. Yang and M. M. Crawford, "Domain adaptation with preservation of manifold geometry for hyperspectral image classification, " IEEE J. Sel. Topics Appl. Earth Observ., vol. 9, no. 2, pp. 543-555, 2016
44. H. Sun, S. Liu, S. Zhou, and H. Zou, "Unsupervised cross-view semantic transfer for remote sensing image classification, " IEEE Geosci. Remote Sens. Lett., vol. 13, no. 1, pp. 13-17, 2016
45. H. Sun, S. Liu, S. Zhou, and H. Zou, "Transfer sparse subspace analysis for unsupervised cross-view scene model adaptation, " IEEE J. Sel. Topics Appl. Earth Observ., 2016. DOI: 10.1109/JSTARS.2015.2500961
46. K. Zhang, B. Schölkopf, K. Muandet, Z. Wang, Z. Zhou, and C. Persello, "Single-source domain adaptation with target and conditional shift, " in Regularization, Optimization, Kernels, and Support Vector Machines, J. A. K. Suykens, M. Signoretto, and A. Argyriou, Eds. Boca Raton, FL: CRC, 2014
47. F. Petitjean, A. Ketterlin, and P. Gancarski, "A global averaging method for dynamic time warping, with applications to clustering, " Pattern Recogn., vol. 44, no. 3, pp. 678-693, 2011
48. G. Jun and J. Ghosh, "Spatially adaptive classification of land cover with remote sensing data, " IEEE Trans. Geosci. Remote Sens., vol. 49, no. 7, pp. 2662-2673, 2011
49. L. Zhang, L. Zhang, D. Tao, and X. Huang, "Sparse transfer manifold embedding for hyperspectral target detection, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 2, pp. 1030-1043, 2014
50. D. Tuia, J. Muñoz-Marí, L. Gómez-Chova, and J. Malo, "Graph matching for adaptation in remote sensing, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 329-341, 2013
51. E. Othman, Y. Bazi, N. Alajlan, H. AlHichri, and F. Melgani, "Threelayer convex network for domain adaptation in multitemporal VHR images, " IEEE Geosci. Remote Sens. Lett., vol. 13, no. 3, 2016
52. F. Pacifici, J. Chanussot, and Q. Du, "2011 GRSS Data Fusion Contest: Exploiting WorldView-2 multi-angular acquisitions, " in Proc. IEEE Int. Geosci. and Remote Sens. Symp (IGARSS), Vancouver, Canada, 2011, pp. 1163-1166
53. L. Bruzzone and D. F. Prieto, "Unsupervised retraining of a maximum-likelihood classifier for the analysis of multitemporal remote-sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 39, no. 2, pp. 456-460, 2001
54. L. Bruzzone and D. F. Prieto, "A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images, " Pattern Recogn. Lett., vol. 23, no. 9, pp. 1063-1071, 2002
55. L. Bruzzone and R. Cossu, "A multiple cascade-classifier system for a robust a partially unsupervised updating of land-cover maps, " IEEE Trans. Geosci. Remote Sens., vol. 40, no. 9, pp. 1984-1996, 2002
56. L. Bruzzone, R. Cossu, and G. Vernazza, "Combining parametric and non-parametric algorithms for a partially unsupervised classification of multitemporal remote-sensing images, " Info. Fusion, vol. 3, no. 4, pp. 289-297, 2002
57. S. Rajan, J. Ghosh, and M. M. Crawford, "Exploiting class hierarchy for knowledge transfer in hyperspectral data, " IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3408-3417, 2006
58. L. Bruzzone, M. Chi, and M. Marconcini, "A novel transductive SVM for semisupervised classification of remote-sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3363-3373, 2006
59. M. Chi and L. Bruzzone, "Semi-supervised classification of hyperspectral images by SVMs optimized in the primal, " IEEE Trans. Geosci. Remote Sens., vol. 45, no. 6, pp. 1870-1880, 2007
60. Z. Sun, C. Wang, H. Wang, and J. Li, "Learn multiple-kernel SVMs for domain adaptation in hyperspectral data, " IEEE Geosci. Remote Sens. Lett., vol. 10, no. 5, pp. 1224-1228, 2013
61. J. M. Leiva-Murillo, L. Gómez-Chova, and G. Camps-Valls, "Multitask remote sensing data classification, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 151-161, 2013
62. L. Gómez-Chova, G. Camps-Valls, J. Muñoz-Marí, and J. Calpe-Maravilla, Semi-supervised image classification with Laplacian support vector machines, " IEEE Geosci. Remote Sens. Lett., vol. 5, no. 3, pp. 336-340, 2008
63. W. Kim and M. M. Crawford, Adaptive classification for hyperspectral image data using manifold regularization kernel machines, " IEEE Trans. Geosci. Remote Sens, vol. 48, no. 11, pp. 4110-4121, 2010
64. L. Bruzzone and M. Marconcini, "Domain adaptation problems: A DASVM classification technique and a circular validation strategy, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 5, pp. 770-787, 2010
65. G. Jun and J. Ghosh, "An efficient active learning algorithm with knowledge transfer for hyperspectral data analysis, " in Proc. IEEE Int. Geosci. and Remote Sens. Symp (IGARSS), Boston, MA, 2008, pp. I-52-I-55
66. S. Rajan, J. Ghosh, and M. M. Crawford, "An active learning approach to hyperspectral data classification, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 4, pp. 1231-1242, 2008
67. G. Matasci, D. Tuia, and M. Kanevski, "SVM-based boosting of active learning strategies for efficient domain adaptation, " IEEE J. Sel. Topics Appl. Earth Observ., vol. 5, no. 5, pp. 1335-1343, 2012
68. B. Settles, Active Learning. San Rafael, CA: Morgan & Claypool, 2012
69. D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Muñoz-Marí, "A survey of active learning algorithms for supervised remote sensing image classification, " IEEE J. Select Topics Signal Processing, vol. 5, no. 3, pp. 606-617, 2011
70. M. M. Crawford, D. Tuia, and H. L. Yang, "Active learning: Any value for classification of remotely sensed data"." Proc. IEEE, vol. 101, no. 3, pp. 593-608, 2013
71. L. Bruzzone, C. Persello, and B. Demir, "Active learning methods in classification of remote sensing images, " in Signal and Image Processing for Remote Sensing, C. Chen, Ed. Boca Raton, FL: CRC, 2012
72. B. Demir, F. Bovolo, and L. Bruzzone, "Detection of land-cover transitions in multitemporal remote sensing images with active learning based compound classification, " IEEE Trans. Geosci. Remote Sens., vol. 50, no. 5, pp. 1930-1941, 2012
73. A. Stumpf, N. Lachiche, J. P. Malet, N. Kerle, and A. Puissant, "Active learning in the spatial-domain for landslide mapping in remote sensing images, " in Proc. Euro. Conf. Machine Learn. (ECML), Active Learning in Real-World Applications Workshop, Bristol, U.K., 2012
74. M. Roy, F. Melgani, A. Ghosh, E. Blanzieri, and S. Ghosh, "Landcover classification of remotely sensed images using compressive sensing having severe scarcity of labeled patterns, " IEEE Geosci. Remote Sens. Lett., vol. 12, no. 6, pp. 1257-1261, 2015
75. N. Alajlan, E. Pasolli, F. Melgani, and A. Franzoso, "Large-scale image classification using active learning, " IEEE. Geosci. Remote Sens. Lett., vol. 11, no. 1, 259-263, 2014
76. P. Blanchart, M. Ferecatu, S. Cui, and M. Datcu, "Pattern retrieval in large image databases using multiscale coarse-to-fine cascaded active learning, " IEEE J. Sel. Topics Appl. Earth Observ., vol. 7, no. 4, 1127-1141, 2014
77. B. Demir, L. Minello, and L. Bruzzone, "Definition of effective training sets for supervised classification of remote sensing images by a novel cost-sensitive active learning method, " IEEE Trans. Geosci. Remote Sens., vol. 52, pp. 1272-1284, Feb. 2014
78. C. Persello, A. Boularis, M. Dalponte, T. Gobakken, E. Naesset, and B. Schölkopf, "Cost-sensitive active learning with lookahead: Optimizing field surveys for remote sensing data classification, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 10, pp. 6652-6664, 2014
79. T. Luo, K. Kramer, D. B. Goldgof, L. O. Hall, S. Samson, A. Remsen, and T. Hopkins, "Active learning to recognize multiple types of plankton, " J. Mach. Learn. Res., vol. 6, 589-613, Apr. 2005