Cost-sensitive and modular land-cover classification based on posterior probability estimates

International Journal of Remote Sensing

Volumn 30, Issue 22, 2009, Pages 5877-5899

  Guerrero Curieses, A ^a   Alaiz Rodriguez R ^b   Cid Sueiro, J ^c  

^a UNIVERSIDAD REY JUAN CARLOS   (Spain)

^b UNIVERSITY OF LEÓN   (Spain)

^c UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

IMPULSE NOISE; LEARNING ALGORITHMS; PROBABILITY; SPECTROSCOPY;

DECISION BOUNDARY; DECISION PROBLEMS; LAND COVER CLASSIFICATION; MISCLASSIFICATION COSTS; NONLINEAR CLASSIFIERS; POSTERIOR CLASS PROBABILITIES; POSTERIOR PROBABILITY; PROBABILITY ESTIMATE;

COST BENEFIT ANALYSIS;

ALGORITHM; ESTIMATION METHOD; IMAGE ANALYSIS; LAND COVER; MAP; PROBABILITY; SATELLITE IMAGERY;

EID: 70449367440     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431160902787695     Document Type: Article

References (69)

1. AGRAWAL, A., KUMAR, N., and RADHAKRISHNA, M., 2007, Multispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation. International Journal of Remote Sensing, 28, pp. 4597-4608.
2. ALBA, J., DOCÍO, L., DOCAMPO, D. and MÁRQUEZ, O., 1999, Growing mixtures network for classification applications. Signal Processing, 76, pp. 43-60.
3. ARRIAZA, J.T., ROJAS, F.G., LÓPEZ, M.P. and CANTÓN, M., 2003, An automatic cloud-masking system using backpro neural nets forAVHRR scenes. IEEE Transactions on Geoscience and Remote Sensing, 41, pp. 826-831.
4. ARRIBAS, J. and CID-SUEIRO, J., 2005, A model selection algorithm for a posteriori probability estimation with neural networks. IEEE Transactions on Neural Networks, 16, pp. 799-809.
5. BENEDIKTSSON, J., PALMASON, J. and SVEINSSON, J., 2005, Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 480-491.
6. BERARDI, V. and ZHANG, G., 1999, The effect of misclassification costs on neural network classifiers. Decision Sciences, 30, pp. 659-682.
7. BISCHOF, H. and LEONARDIS, A., 1998, Finding optimal neural networks for land use classification. IEEE Transactions on Geoscience and Remote Sensing, 36, pp. 337-341.
8. BISCHOF, H., SCHNEIDER,W. and PINZ, A., 1992, Multispectral classification of Landsat images using neural networks. IEEE Transactions on Geoscience and Remote Sensing, 30, pp. 482-490.
9. BOBROWSKY, L. and SKLANSKY, J., 1995, Linear classifiers by window training. IEEE Transactions on System Science and Cybernetics, 25, pp. 1-9.
10. BOGDANOV, A., SANDVEN, S., JOHANNESSEN, O., ALEXANDROV, V. and BOBYLEV, L., 2005, Multisensor approach to automated classification of sea ice image data. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 1648-1664.
11. BRADFORD, J.,KUNZ, C.,KOHAVI, C., BRUNK,R. and BRODLEY, C., 1998, Pruning Decision Trees with Misclassification Costs, Lecture Notes in Artificial Intelligence (Berlin: Springer-Verlag).
12. BRUZZONE, L., 2000, An approach to feature selection and classification of remote sensing images based on the Bayes rule for minimum cost. IEEE Transactions on Geoscience and Remote Sensing, 38, pp. 429-438.
13. BRUZZONE, L.,MARCONCINI, M.,WEGMULLER,U. andWIESMANN, A., 2004, An advanced system for the automatic classification of multitemporal sar images. IEEE Transactions on Geoscience and Remote Sensing, 42, pp. 1321-1334.
14. CAMPS-VALLS, G. and BRUZZONE, L., 2005, Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 1351-1362.
15. CHRISTODOULOU, C.,MICHAELIDES, S. and PATTICHIS, C., 2003, Multifeature texture analysis for the classification of clouds in satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 41, pp. 2662-2668.
16. CID-SUEIRO, J. and SANCHO-GÓMEZ, J., 2001, Satured perceptrons for maximum margin and minimum misclassification error. Neural Processing Letters, 14, pp. 217-226.
17. CONGALTON, R.G., 1991,Areview of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, pp. 35-46.
18. DIXON, D., and CANDADE, N., 2008, Multispectral landuse classification using neural networks and support vector machines: one or the other, or both. International Journal of Remote Sensing, 29, pp. 1185-1206.
19. DO-TU, H. and INSTALLE, M., 1978, Learning algorithms for non-parametric solution to be minimum error classification problem. IEEE Transactions on Computers, 27, pp. 648-659.
20. DUDA, R. and HART, P., 1973, Pattern Classification and Scene Analysis (New York: John Wiley).
21. FANG, H. and LIANG, S., 2003, Retrieving leaf area index with a neural network method: Simulation and validation. IEEE Transactions on Geoscience and Remote Sensing, 41, pp. 2052-2062.
22. FOODY, G.M., 2008, RVM-based multi-class classification of remotely sensed data. International Journal of Remote Sensing, 29, pp. 1817-1823.
23. FOODY, G.M. and COX, D.P., 1994, Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership fucntions. International Journal of Remote Sensing, 15, pp. 619-631.
24. FRATE, F.D. and SOLIMINI, D., 2004, On neural network algorithms for retrieving forest biomass from SAR data. IEEE Transactions on Geoscience and Remote Sensing, 42, pp. 24-34.
25. FUKUNAGA, K., 1990, Introduction to Statistical Pattern Recognition, 2nd edn (San Diego: Academic Press).
26. GUERRERO-CURIESES, A., ALAIZ-RODRIGUEZ, R. and CID-SUEIRO, J., 2005, Loss functions to combine learning and decision in multiclass problems. Neurocomputing, 69, pp. 3-17.
27. GUERRERO-CURIESES, A., CID-SUEIRO, J., ALAIZ-RODRIGUEZ, R. and FIGUEIRAS-VIDAL, A., 2004, Local estimation of posterior class probabilities to minimize classification errors. IEEE Transactions on Neural Networks, 15, pp. 309-317.
28. HASSIBI, B. and STORK, D., 1993, Second order derivatives for network pruning: Optimal brain surgeon. In Advances in Neural Information Processing Systems, Vol. 5, S.J. Hanson, J.D. Cowan, and C.L. Giles, (Eds.) (SanMateo, CA: Morgan Kaufmann), pp. 164-171. Available online at: http://citeseer.nj.nec.com/hassibi93second.html
29. HONG, S., MOON, W., HONG-YUL, P. and CHOI, G.-H., 2002, Data fusion of multiple polarimetric SAR images using discrete wavelet transform (DWT). IEEE International Geoscience and Remote Sensing Symposium, June 2002; GARSS 002, 6, June, pp. 3323-3325.
30. JENSEN, J., QIU, F. and PATTERSON, K., 2001, A neural network image interpretation system to extract rural and urban land use and land cover information from remote sensor data. Geocarto International, 16, pp. 1-10.
31. JOACHIMS, T., 1999, Making large-scale SVM learning practical. Advances in kernel methods - support vector learning. Technical Report, B. Schölkopf, C. Burges and A. Smola (Eds.) (Cambridge, MA: MIT Press).
32. JUANG, B. and KATAGIRI, S., 1992, Discriminative learning for minimum error classification. IEEE Transactions on Signal Processing, 40, pp. 3043-3054.
33. KULKARNI, A. and MCCASLIN, S., 2004, Knowledge discovery from multispectral satellite images. IEEE Geoscience and Remote Sensing Letters, 1, pp. 246-250.
34. KUMAR, A., BASU, S. and MAJUMDAR, K., 1997, Robust classification of multispectral data using multiple neural networks and fuzzy integral. IEEE Transactions on Geoscience and Remote Sensing, 35, pp. 787-790.
35. LECUN, Y.,DENKER, J., SOLLA, S.,HOWARD, R. and JACKEL, L., 1990, Optimal brain damage. In Advances in Neural Information Processing Systems II, D.S. Touretzky (Ed.) (San Mateo, CA: Morgan Kauffman). Available online at: http://citeseer.nj.nec.com/lecun90optimal.html
36. LI, Z., KHANANIAN, A., FRASER, R. and CIHLAR, J., 2001, Automatic detection of fire smoke using artificial neural networks and threshold approaches applied to AVHRR imagery. IEEE Transactions on Geoscience and Remote Sensing, 39, pp. 1859-1870.
37. LIU, C., FRAZIER, P. and KUMAR, L., 2007, Comparative assessment of the measures of thematic classification accuracy. Remote Sensing of Environment, 107, pp. 606-616.
38. MARGINEANTU, D. and DIETTERICH, T., 2000, Bootstrap methods for the cost-sensitive evaluation of classifiers. In Proceedings of 17th International Conference on Machine Learning, (San Francisco, CA: Morgan Kaufmann), pp. 583-590.
39. MELGANI, F. and BRUZZONE, L., 2004, Classification of hyperspectral remote-sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, pp. 1778-1790.
40. MERCIER, G.,HUBERT-MOY, L.,HOUET, T. and GOUÉRY, P., 2005, Estimation and monitoring of bare soil/vegetation ratio with spot vegetation and HRVIR. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 348-354.
41. MILLER, J., GOODMAN, R. and SMYTH, P., 1991, Objective functions for probability estimation. In Proceedings of the International Conference on Neural Networks, Vol. 1, pp. 881-886.
42. MUCHONEY, D. and WILLIAMSON, J., 2001, A Gaussian adaptive resonance theory neural network classification algorithm applied to supervised land cover mapping using multitemporal vegetation index data. IEEE Transactions on Geoscience and Remote Sensing, 39, pp. 1969-1977.
43. OTI, K., UENISHI, T.M., OMASA, K. and TAMURA, M., 2004, Accuracy of land cover area estimated from coarse spatial resolution images using an unmixing method. International Journal of Remote Sensing, 25, pp. 1673-1683.
44. PAZZANI, M., MERZ, C., MURPHY, P., ALI, K., HUME, T. and BRUNK, C., 1994, Reducing misclassification costs. In Proceedings of the 11th International Conference on Machine Learning, (San Francisco, CA: M. Kaufmann), pp. 217-225.
45. PEARLMUTTER, B. and HAMPSHIRE, J., 1990, Equivalence proofs for multilayer perceptron classifiers and the Bayesian discriminant function. In Proceedings of 1990 Connectionist Models Summer School (San Diego, CA: Morgan Kauffmann), pp. 159-172.
46. PELLIZZERI, T., OLIVER, C., LOMBARDO, P. and BUCCIARELLI, T., 2002, Improved classification of SAR images by segmentation and fusion with optical images. In RADAR 2002, 15-17 October 2002, pp. 158-161.
47. PESARESI,M. and BENESIKTSSON, J., 2001, A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 39, pp. 309-320.
48. POHL, C. and GENDEREN, J.V., 1998, Multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, 19, pp. 823-854.
49. POIRAZI, P., NEOCLEOUS, C., PATTICHIS, C. and SCHIZAS, C., 2004, Classification capacity of a modular neural network implementing neurally inspired architecture and training rules. IEEE Transactions on Neural Networks, 15, pp. 597-612.
50. QIAN, Y., ZHANG, K. and QIU, F., 2005, Spatial contextual noise removal for post classification smoothing of remotely sensed images. In Proceedings of the 2005 ACM Symposium on Applied Computing, Santa Fe, New Mexico (New York: ACM), pp. 524-528.
51. RAUDYS, S., 1998, Evolution and generalization of a single neurone I. Single layer perceptron as seven statistical classifiers. Neural Networks, 11, pp. 283-296.
52. RAUDYS, S., 1998, Evolution and generalization of a single neurone II. Complexity of statistical classifiers and sample size considerations. Neural Networks, 11, pp. 297-313.
53. RICHARD,M. and LIPPMANN, R., 1991, Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Computation, 3, pp. 461-483.
54. RIZVI, S. and NASRABADI, N., 2003, Fusion techniques for automatic target recognition. In Proceedings 32nd Applied Imagery Pattern Recognition Workshop, 15-17 October 2003, pp. 27-32.
55. RUCK, D., ROGERS, S., KABRISKY, M., OXLEY, M. and SUTER, B., 1990, The multilayer perceptron as an approximation to a Bayes optimal discriminant function. IEEE Transactions on Neural Networks, 1, pp. 296-298.
56. SERPICO, S., BRUZZONE, L. and ROLI, F., 1996, An experimental comparison of neural and statistical non-parametric algorithms for supervised classification of remote-sensing images. Pattern Recognition Letters, 17, pp. 1331-1341.
57. SMITS, P., DELLEPIANE, S. and SCHOWENGERDT, R., 1999, Quality assesment of image classification algorithms for land-cover mapping: A review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20, pp. 1461-1486.
58. SOILLE, P., 2003, Morphological Image Analysis: Principles and Applications, 2nd edn (Berlin: Springer-Verlag).
59. SOILLE, P. and PESARESI, M., 2002, Advances in mathematical morphology applied to geoscience and remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 40, pp. 2042-2055.
60. SOLAIMAN, B., KOFFI, R.,MOUCHOT,M. and HILLION, A., 1998, An information fusion method for multispectral image classification postprocessing. IEEE Transactions on Geoscience and Remote Sensing, 36, pp. 395-406.
61. SRIVASTAVA, A., OZA, N. and STROEVE, J., 2005, Virtual sensors: Using data mining techniques to efficiently estimate remote sensing spectra. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 590-600.
62. TIAN, B. and AZIMI-SADJADI, M., 2001, Comparison of two different PNN training approaches for satellite cloud data classification. IEEE Transactions on Neural Networks, 12, pp. 164-168.
63. TIAN, B., SHAIKH, M., AZIMI-SADJADI, M., HAAR, T. and REINKE, D., 1999, A study of cloud classification with neural networks using spectral and textural features. IEEE Transactions on Neural Networks, 10, pp. 138-151.
64. TOMAS, I., 1980, Spatial postprocessing of spectrally classified landsat data. Photogrammetic Engineering and Remote Sensing, 46, pp. 1201-1206.
65. TON, J., STICKLEN, J. and JAIN, A., 1991, Knowledge-based segmentation of Landsat images. IEEE Transactions on Geoscience and Remote Sensing, 29, pp. 222-231.
66. TOWNSHEND, J., 1986, The enhancement of computer classification by logical smoothing. Photogrammetic Engineering and Remote Sensing, 52, pp. 213-221.
67. VAPNIK, V., 1995, The Nature of the Statistical Learning Theory (New York: Springer-Verlag). VERHOEYE, J. and WULF, R.D., 2002, Land cover mapping at sub-pixel scales using linear optimization techniques. Remote Sensing of Environment, 79, pp. 96-104.
68. ZELLNER, A., 1986, Bayesian estimation and prediction using asymmetric loss functions. Journal of the American Statistical Association, 81, pp. 446-451.
69. ZHANG, Y., 2001, Detection of urban housing development by fusing multisensor satellite data and performing spatial feature post-classification. International Journal of Remote Sensing, 22, pp. 3339-3355.