Accuracy measures for fuzzy classification in remote sensing
| dc.book.title | IPMU 2006 : Information Processing and Management of Uncertainty in Knowledge-Based Systems : proceedings : Eleventh International Conference, Paris, Les Cordeliers, July 2-7, 2006. | |
| dc.contributor.author | Gómez González, Daniel | |
| dc.contributor.author | Montero De Juan, Francisco Javier | |
| dc.contributor.author | Biging, Greg | |
| dc.date.accessioned | 2023-06-20T13:41:37Z | |
| dc.date.available | 2023-06-20T13:41:37Z | |
| dc.date.issued | 2006 | |
| dc.description | International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (11ª. 2006. París) | |
| dc.description.abstract | Over the last decades, many fuzzy classification algorithms have been proposed for image classification,and in particular to classify those images obtained by remote sensing. But relatively little effort has been done to evaluate goodness or effectiveness of such algorithms. Such a problem is most of the times solved by means of a subjective evaluation, meanwhile in the crisp case quality evaluation can be based upon an error matrix, in which the reference data set (the expert classi-fication) and crisp classifiers data set are been compared using specific accuracy measures. In this paper,some of these measures are translated into the fuzzy case, so that more general accuracy measures between fuzzy classifiers and the reference data set can be considered. | en |
| dc.description.department | Depto. de Estadística e Investigación Operativa | |
| dc.description.faculty | Fac. de Ciencias Matemáticas | |
| dc.description.refereed | TRUE | |
| dc.description.sponsorship | Gobierno de España | |
| dc.description.status | pub | |
| dc.eprint.id | https://eprints.ucm.es/id/eprint/29106 | |
| dc.identifier.isbn | 2-84254-112-X | |
| dc.identifier.officialurl | http://www.math.s.chiba-u.ac.jp/~yasuda/open2all/Paris06/IPMU2006/HTML/FINALPAPERS/P608.PDF | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14352/53408 | |
| dc.language.iso | eng | |
| dc.page.final | 1563 | |
| dc.page.initial | 1556 | |
| dc.publication.place | Paris | |
| dc.publisher | EDK | |
| dc.relation.projectID | MTM2005-08982 | |
| dc.rights.accessRights | open access | |
| dc.subject.cdu | 004.8 | |
| dc.subject.keyword | Accuracy assessment | |
| dc.subject.keyword | Remote sensing | |
| dc.subject.keyword | Fuzzy classification | |
| dc.subject.ucm | Inteligencia artificial (Informática) | |
| dc.subject.unesco | 1203.04 Inteligencia Artificial | |
| dc.title | Accuracy measures for fuzzy classification in remote sensing | en |
| dc.type | book part | |
| dc.volume.number | 3 | |
| dcterms.references | [1] A. Amo, D. Gomez, J. Montero and G. Biging (2001): Relevance and redundancy in fuzzy classification systems. Mathware and Soft Computing 8:203-216 [2] A. Amo, J. Montero, G. Biging and V. Cutello (2004): Fuzzy classification systems. European Journal of Operational 156:459-507. [3] E. Binaghi, P.A. Brivio, P. Ghezzi and . Rampini (1999): A fuzzy set based accuracy assessment of soft classification. Pattern Recognition Letters 20:935948. [4] R.G. Congalton and K. Green (1999): Assessing the Accuracy of Remote Sensed Data: Principles and Practices. Lewis Publishers, London. [5] R.G. Congalton and G. Biging (1992): A pilot study evaluating ground reference data collection efforts for use in forestry inventory. Photogrametic Engineering and Remote Sensing 58:1669- 1671. [6] D. Dubois and H. Prade (1997): The three semantics of fuzzy sets. Fuzzy Sets and Systems 90:141-150. [7] G.M. Foody (1996): Approaches for the production and evaluation of fuzzy land cover classification from remotely sense data. International Journal of RRemote Sensing 17:1317-1340. [8] L. Goldfarb (1992): What is distance and why we do need the metric model for pattern learning. Pattern Recognition 25:431-438. [9] D. Gomez, J. Montero, J. Yañez, C.Poidomani: A graph coloring algorithm approach for image segmentation. Omega (to appear). [10] J.G. Pachon, D. Gomez, J. Montero and J. Yanez (2003): Soft dimension theory. Fuzzy set and Systems 137:137-149. [11] J.G. Pachon, D. G´omez, J. Montero and J. Yañez (2003): Searching for the dimension of binary valued preference relations. International Journal of Approximate Reasoning 33:133-157. [12] M. Laba, S.K. Gregory, J. Braden, D. Ogurcak, E. Hill,E. Fegraus, J. Fiore and S.D. DeGloria (2002): Conventional and fuzzy accuracy assessment of the New York Gap Analysis Project land cover map. Remote Sensing of Enviroment 81:443-455. [13] C. Macharis and J.P. Brans (1998): The GDSS Promethee procedure. Journal of Decision Systems 7:283-307. [14] C.R. Rao (1965): Linear Statistical Inference and its Applications. Wiley, New York. [15] A. Rosenfeld (1985): Distances between fuzzy sets.Pattern Recognition Letters 3:229-233. [16] B. Roy (1990): Decision aid and decision making. European Journal of Operational Research 45:324-331. [17] E.H. Ruspini (1969): A new approach to clustering. Information and Control 15:22-32. [18] T.L. Saaty (1994): Fundamentals of Decision Making with the Analytic Hierarchy Process. RWS Publications, Pittsburgh (Revised in 2000). [19] S.V. Stehman and R.L. Czaplewski (2003): Introduction to sppecial issues on map accuracy. Enviromental and Ecological Statistics 10:301-308. [20] C.E. Woodcock and S. Gopal (2000):Fuzzy set theory and thematics maps:accuracy assessment and area estimation.International Journal Geographical Information Science 14:153-172. [21] L.A. Zadeh (1965): Fuzzy sets. Informations Sciences 8:338-353. | |
| dspace.entity.type | Publication | |
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