Publication: Fuzzy sets in remote sensing classification.
Loading...
Full text at PDC
Publication Date
2008
Authors
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer-Verlag
Abstract
Supervised classification in remote sensing is a very complex problem and involves steps of different nature, including a serious data preprocessing. The final objective can be stated in terms of a classification of isolated pixels between classes, which can be either previously known or not (for example, different land uses), but with no particular shape nither well defined borders. Hence, a fuzzy approach seems natural in order to capture the structure of the image. In this paper we stress that some useful tools for a fuzzy classification can be derived from fuzzy coloring procedures, to be extended in a second stage to the complete non visible spectrum. In fact, the image is considered here as a fuzzy graph defined on the set of pixels, taking advantage of fuzzy numbers in order to summarize information. A fuzzy model is then presented, to be considered as a decision making aid tool. In this way we generalize the classical definition of fuzzy partition due to Ruspini, allowing in addition a first evaluation of the quality of the classification in this way obtained, in terms of three basic indexes (measuring covering, relevance and overlapping of our family of classes).
Description
UCM subjects
Unesco subjects
Keywords
Citation
Amo A, Gomez D, Montero J, Biging G (2001) Relevance and redundancy in fuzzy classification systems. Mathware Soft Comput 8:203–216
Amo A, Montero J, Biging G, Cutello V (2004) Fuzzy classification systems. Eur J Oper Res 156:459–507
AmoA, Montero J, Molina E (2001) Representation of consistent recursive rules. Eur J Oper Res 130:29–53
Binahi E, RampiniA(1993) Fuzzy decision-making in the classification of multisource remote-sensing data. Opt Eng 32:1193–1204
BensaidAM, Hall LO, Bezdek JC et al. (1996)Validity-guided (re)clustering with applications to image segmentation. IEEE Trans Fuzzy Systems 4:112–123
Bezdek JC, Harris JD (1978) Fuzzy partitions y Relations, an axiomatic basis for clustering. Fuzzy Sets Systems 1:111–127
Calvo T, Mayor G, Mesiar R (2002) Aggregation operators. Physica-Verlag, Heidelberg
Cutello C, Montero J (1975) Hierarchical aggregation of OWA operators: basic measures and related computational problems. Uncertainty, fuzziness and knowledge-based systems 3:17–26
Cutello V, Montero J (1999) Recursive connective rules. Int J Intell Systems 14:3–20
Cogalton RG, Green K (1999) Assessing the accuracy of remote sensed data: principles and practices. Lewis publishers, London, New York
Dubois D, Prade H (1983) Ranking fuzzy numbers in the setting of possibility theory. Inf Sci 30:183–224
Fisher PF, Pathirana S (1990) The evaluation of fuzzy membership of land cover classes in the suburban zone. Remote Sens Environ 34:121–132
FoodyGM(1999) The continuum of classification fuzziness in thematic mappings. hotogrammetr Eng Remote Sens 65:443–451
Foody GM (1996) Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data. Int J Remote Sens 17:1317–1340
Foody GM, Cox DP (1994) Sub-pixel land-cover composition estimation using a linear mixturemodel and fuzzy membership functions. Int J Remote Sens 15:619–631
Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 11:773–781
Gómez D, Montero J (2004) A discussion on aggregation operators. Kybernetika 40:107–120
Gómez D, Montero J, Yáñez J, Poidomani C (2007) A graph coloring algorithm approach for image segmentation. Omega 35:173–183
Gómez D, Montero J, Yáñez J (2006) A coloring fuzzy graph approach for image classification. Inf Sci 176:3645–3657
Klement EP, Mesiar R, Pap E (2000) Triangular Norms. Kluwer, Dordrecht Iancu I (1999) Connectives for fuzzy partitions. Fuzzy Sets Systems 101:509–512
Pardalos PM, Mavridou T, Xue J (1998) The Graph Coloring Problem: A Bibliographic Survey. In: Du DZ, Pardalos PM (eds) Handbook of combinatorial optimization, vol 2. Kluwer, Boston, pp 331–395
Ruspini EH (1969) A new approach to clustering. Inf Control 15:22–32
Ruspini EH (1970) Numerical methods for fuzzy clustering. Inf Sci 2, p 319
Thiele H (1996a) A characterization of Ruspini-partitions by similarity relations. In: roceedings of the IPMU’96 conference, pp 389–394
Thiele H (1996b) A characterization of arbitrary Ruspini-partitions by fuzzy similarity relations. In: Proceedings of the IPMU’96 conference, pp 131–134
Wasilakos A, Stathakis D, Wang F (1990) Fuzzy supervised classification of remote-sensing images. Soft Comput 9(5):332–340
Wang F (1990) Fuzzy supervised classification of remote-sensing images. IEEE Trans Geosci Remote Sens 28:194–201
Yager RR (1993) Families of OWA operators. Fuzzy Sets Systems 59:125–148
Zadeh LA (1965) Fuzzy sets. Inf Control 8:378–453