A Review of Modern Approaches to Classification of Remote Sensing Data

Chapter
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 18)

Abstract

This chapter presents an extensive review on the techniques proposed in the recent literature for the classification of remote sensing (RS) images. Automatic classification techniques for obtaining land-cover maps from RS images are usually based on supervised learning methods. Accordingly, we focus our attention on supervised techniques for the classification of different types of RS images acquired by new generation satellite sensors. Initially we analyze the critical problems related to different types of RS data and review the classification techniques that can overcome these problems. Then, the most recent methodological developments related to classification techniques in RS are addressed by focusing on semisupervised learning, active learning and domain adaptation approaches. Finally, the most promising research directions in RS data classification are discussed.

References

  1. Bahirat K, Bovolo F, Bruzzone L, Chaudhuri S (2010) A novel domain adaptation maximum likelihood classifier for updating land-cover maps in complex scenarios. In: Proceedings of the SPIE conference on image and signal processing for remote sensing, Toulouse, FranceGoogle Scholar
  2. Bandyopadhyay S, Pal SK (2001) Pixel classification using variable string genetic algorithms with chromosome differentiation. IEEE Trans Geosci Remote Sens 39(2):303–308CrossRefGoogle Scholar
  3. Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434Google Scholar
  4. Bennett KP, Demiriz A (1998) Semi-supervised support vector machines. Proc Adv Neural Inform Process Syst 10:368–374Google Scholar
  5. Binaghi E, Gallo I, Pepe M (2003) A neural adaptive model for feature extraction and recognition in high resolution remote sensing imagery. Int J Remote Sens 24(20):3947–3959CrossRefGoogle Scholar
  6. Bischof H, Leona A (1998) Finding optimal neural networks for land use classification. IEEE Trans Geosci Remote Sens 36(1):337–341CrossRefGoogle Scholar
  7. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Haussler D (ed) Proceedings of the in 5th annual ACM workshop on COLT, Pittsburgh, PA, pp 144–152Google Scholar
  8. Bruzzone L, Carlin L (2006) A multilevel context-based system for classification of very high spatial resolution images. IEEE Trans Geosci Remote Sens 44(9):2587–2600CrossRefGoogle Scholar
  9. Bruzzone L, Cossu R (2002) A multiple cascade-classifier system for a robust a partially unsupervised updating of land-cover maps. IEEE Trans Geosci Remote Sens 40(9):1984–1996CrossRefGoogle Scholar
  10. Bruzzone L, Fernández-Prieto D (1999) A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images. IEEE Trans Geosci Remote Sens 37(2):1179–1184CrossRefGoogle Scholar
  11. Bruzzone L, Fernandez Prieto D (2001) Unsupervised retraining of a maximum-likelihood classifier for the analysis of multitemporal remote-sensing images. IEEE Trans Geosci Remote Sens 39(2):456–460CrossRefGoogle Scholar
  12. Bruzzone L, Fernandez Prieto D (2002) A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images. Pattern Recogn Lett 23(9):1063–1071CrossRefGoogle Scholar
  13. Bruzzone L, Marconcini M (2010) Domain adaptation problems: a DASVM classification technique and a circular validation strategy. IEEE Trans Pattern Anal Mach Intell 32(5):770–787CrossRefGoogle Scholar
  14. Bruzzone L, Persello C (2009) A novel context-sensitive semisupervised SVM classifier robust to mislabeled training samples. IEEE Trans Geosci Remote Sens 47(7):2142–2154CrossRefGoogle Scholar
  15. Bruzzone L, Chi M, Marconcini M (2006) A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Trans Geosci Remote Sens 44(11):3363–3373CrossRefGoogle Scholar
  16. Camps-Valls G, Bruzzone L (2005) Kernel-based methods for hyperspectral image classification. IEEE Trans Geosci Remote Sens 43(6):1351–1362CrossRefGoogle Scholar
  17. Camps-Valls G, Gómez-Chova L, Calpe J, Soria E, Martín JD, Alonso L, Moreno J (2004) Robust support vector method for hyperspectral data classification and knowledge discovery. IEEE Trans Geosci Remote Sens 42(7):1530–1542CrossRefGoogle Scholar
  18. Camps-Valls G, Gomez-Chova L, Munoz-Mari J, Vila-Frances J, Calpe-Maravilla J (2006) Composite kernels for hyperspectral image classification. IEEE Geosci Remote Sens Lett 3(1):93–97CrossRefGoogle Scholar
  19. Camps-Valls G, Bandos T, Zhou D (2007) Semi-supervised graph-based hyperspectral image classification. IEEE Trans Geosci Remote Sens 45(10):3044–3054CrossRefGoogle Scholar
  20. Carleer A, Debeir O, Wolff E (2004) Comparison of very high spatial resolution satellite image segmentation. In: Proceedings of SPIE conference image and signal processing remote sensing, vol 5238. Bellingham, Washington, USA, IX, pp 532–542Google Scholar
  21. Chen X, Fang T, Huo H, Li D (2011) Graph-based feature selection for object-oriented classification in VHR airborne imagery. IEEE Trans Geosci Remote Sens 49(1):353–365CrossRefGoogle Scholar
  22. Chi M, Bruzzone L (2005) A semilabeled-sample-driven bagging technique for ill-posed classification problems. IEEE Geosci Remote Sens Lett 2(1):69–73CrossRefGoogle Scholar
  23. Chi M, Bruzzone L (2006) An ensemble-driven k-NN approach to ill-posed classification problems. Pattern Recognit Lett 27(4):301–307CrossRefGoogle Scholar
  24. Chi M, Bruzzone L (2007) Semisupervised classification of hyperspectral images by SVMs optimized in the primal. IEEE Trans Geosci Remote Sens 45(6):1870–1880CrossRefGoogle Scholar
  25. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, CambridgeGoogle Scholar
  26. Dalla Mura M, Benediktsson JA, Waske B, Bruzzone L (2010) Morphological attribute profiles for the analysis of very high resolution images. IEEE Trans Geosci Remote Sens 48(10):3747–3762CrossRefGoogle Scholar
  27. Demir B, Ertürk S (2007) Hyperspectral image classification using relevance vector machines. IEEE Geosci Remote Sens Lett 4(4):586–590CrossRefGoogle Scholar
  28. Demir B, Persello C, Bruzzone L (2011a) Batch mode active learning methods for the interactive classification of remote sensing images. IEEE Trans Geosci Remote Sens 49(3):1014–1031CrossRefGoogle Scholar
  29. Demir B, Bovolo F, Bruzzone L (2011b) Active-learning based cascade classification of multitemporal images for updating land-cover maps. In: Proceedings of the 6th international workshop on the analysis of multi-temporal remote sensing images, Trento, Italy, pp 57–60, July 2011Google Scholar
  30. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New YorkGoogle Scholar
  31. Dundar MM, Landgrebe DA (2004) A cost-effective semisupervised classifier approach with kernels. IEEE Trans Geosci Remote Sens 42(1):264–270CrossRefGoogle Scholar
  32. Farag A, Mohamed R, El-Baz A (2005) A unified framework for map estimation in remote sensing image segmentation. IEEE Trans Geosci Remote Sens 43(7):1617–1634CrossRefGoogle Scholar
  33. Giacinto G, Bruzzone L (2000) Combination of neural and statistical algorithms for supervised classification of remote-sensing images. Pattern Recognit Lett 21(5):399–405CrossRefGoogle Scholar
  34. Gómez L, Camps-Valls G, Muñoz-Marí J, Calpe-Maravilla J (2008) Semisupervised image classification with Laplacian support vector machines. IEEE Geosci Remote Sens Lett 5(3):336–340CrossRefGoogle Scholar
  35. Gualtieri JA, Chettri SR, Cromp RF, Johnson LF (1999) Support vector machine classifiers as applied to AVIRIS data. In: Summaries 8th JPL airborne earth science workshop, JPL Publication. Pasadena, California, USA, 99–17, pp 217–227Google Scholar
  36. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Englewood CliffsGoogle Scholar
  37. Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23(4):725–749CrossRefGoogle Scholar
  38. Hughes GF (1968) On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theory IT-14(1):55–63CrossRefGoogle Scholar
  39. Kruse FA, Lefkoff AB, Boardman JB, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AFH (1993) The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ 44(2/3):145–163CrossRefGoogle Scholar
  40. Li J, Bioucas-Dias JM, Plaza A (2011) Semi-supervised hyperspectral image classification using a new (soft) sparse multinomial logistic regression model In: Proceedings of the 3rd workshop on hyperspectral image and signal processing: evolution in remote sensing, LisbonGoogle Scholar
  41. Li J, Bioucas-Dias J, Plaza A (2012) Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields. IEEE Trans Geosci Remote Sens 50(3):809–823CrossRefGoogle Scholar
  42. Liu X, Li X, Liu L, He J (2008) An innovative method to classify remote sensing image using ant colony optimization. IEEE Trans Geosci Remote Sens 46(12):4198–4208CrossRefGoogle Scholar
  43. Liu A, Jun G, Ghosh J (2009a) Spatially cost-sensitive active learning. In: SIAM International Conference on Data Mining (SDM), Sparks, Nevada, USA, pp 814–825Google Scholar
  44. Liu A, Jun G, Ghosh J (2009b) Active learning of hyperspectral data with spatially dependent label acquisition costs. In: Proceedings of the IEEE international geoscience and remote sensing symposium, Cape Town, South Africa, pp V-256–V-259Google Scholar
  45. Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790CrossRefGoogle Scholar
  46. Mika S, Rätsch G, Schölkopf B, Smola A, Weston J, Müller K-R (1999) Invariant feature extraction and classification in kernel spaces. In: Advances in neural information processing systems, vol 12. MIT Press, Cambridge, MAGoogle Scholar
  47. Mitra P, Shankar BU, Pal SK (2004) Segmentation of multispectral remote sensing images using active support vector machines. Pattern Recognit Lett 25(9):1067–1074CrossRefGoogle Scholar
  48. Mott C, Andresen T, Zimmermann S, Schneider T, Ammer U (2002) Selective region growing-an approach based on object oriented classification routine. Proc Int Geosci Remote Sens Symp 3:1612–1614CrossRefGoogle Scholar
  49. Murat Dundar M, Landgrebe A (2004) A cost-effective semisupervised classifier approach with kernels. IEEE Trans Geosci Remote Sens 42(1):264–270CrossRefGoogle Scholar
  50. Patra S, Bruzzone L (2011) A fast cluster-based active learning technique for classification of remote sensing images. IEEE Trans Geosci Remote Sens 49(5):1617–1626CrossRefGoogle Scholar
  51. Persello C, Bruzzone L (2011) A novel active learning strategy for domain adaptation in the classification of remote sensing images. In: Proceedings of the IEEE international geoscience and remote sensing symposium, Vancouver, Canada, pp 3720–3723Google Scholar
  52. Pesaresi M, Benediktsson JA (2001) A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Trans Geosci Remote Sens 39(2):309–320CrossRefGoogle Scholar
  53. Rajan S, Ghosh J, Crawford MM (2008) An active learning approach to hyperspectral data classification. IEEE Trans Geosci Remote Sens 46(4):1231–1242CrossRefGoogle Scholar
  54. Samaniego L, Bárdossy A, Schulz K (2008) Supervised classification of remotely sensed imagery using a modified k-NN technique. IEEE Trans Geosci Remote Sens 46(7):2112–2125CrossRefGoogle Scholar
  55. Schölkopf B, Smola A (2001) Learning With kernels—support vector machines, regularization, optimization and beyond. MIT Press, Cambridge, MAGoogle Scholar
  56. Schowengerdt RA (2002) Remote sensing. Models and methods for image processing, 2nd edn. Academic, NorwellGoogle Scholar
  57. Shackelford AK, Davis CH (2003) A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Trans Geosci Remote Sens 41(10):2354–2363CrossRefGoogle Scholar
  58. Tarabalka Y, Chanussot J, Benediktsson JA, Angulo J, Fauvel M (2008) Segmentation and classification of hyperspectral data using watershed. In: Proceedings of the IGARSS, Boston, MA, pp III-652–III-655Google Scholar
  59. Tuia D, Pacifici F, Kanevski M, Emery W (2009a) Classification of very high spatial resolution imagery using mathematical morphology and support vector machines. IEEE Trans Geosci Remote Sens 47(11):3866–3879CrossRefGoogle Scholar
  60. Tuia D, Ratle F, Pacifici F, Kanevski M, Emery WJ (2009b) Active learning methods for remote sensing image classification. IEEE Trans Geosci Remote Sens 47(7):2218–2232CrossRefGoogle Scholar
  61. Unsalan C, Boyer KL (2004) Classifying land development in high resolution panchromatic satellite images using straight-line statistics. IEEE Trans Geosci Remote Sens 42(4):907–919CrossRefGoogle Scholar
  62. van de Vlag DE, Stein A (2007) Incorporating uncertainty via hierarchical classification using fuzzy decision trees. IEEE Trans Geosci Remote Sens 45(1):237–245CrossRefGoogle Scholar
  63. Vapnik VN (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar
  64. Vapnik VN (1999) The nature of statistical learning theory, 2nd edn. Springer, BerlinGoogle Scholar
  65. Yang H, van der Meer F, Bakker W, Tan ZJ (1999) A back-propagation neural network for mineralogical mapping from AVIRIS data. Int J Remote Sens 20(1):97–110CrossRefGoogle Scholar
  66. Zhou D, Huang J, Schölkopf B (2004) Learning with local and global consistency. In: Thrun S, Thrun S, Saul L, Schölkopf B (eds) Advances in neural information processing system, vol 16. MIT Press, Cambridge, MA, pp 321–328Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly

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