Gómez, D. and Montero de Juan, Francisco Javier and Binging, Gregory (2008) Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics. Pure and Applied Geophysics, 165 (8). pp. 1555-1575. ISSN 0033-4553
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Official URL: http://www.springerlink.com/content/f0673477072v770w/
This paper puts together some techniques that have been previously developed by the authors,but separately, relative to fuzzy classification within a remote sensing setting. Considering that each image can be represented as a graph that defines proximity between pixels, certain distances between the characteristic of contiguous pixels are defined on such a graph, so a segmentation of the image into homogeneous regions can be produced by means of a particular algorithm. Such a segmentation can be then introduced as information, previously to any classification procedure, with an expected significative improvement. In particular, we consider specific measures in order to quantify such an improvement. This approach is being illustrated with its application into a particular land surface problem.
|Uncontrolled Keywords:||Classification; Fuzzy sets, fuzzy partition; Multicriteria analysis; Image segmentation.|
|Subjects:||Sciences > Computer science > Artificial intelligence|
AMO, A., GOMEZ, D., MONTERO, J., and BIGING, G. (2001), Relevance and redundancy in fuzzy classification systems, Mathware and Soft Computing 8, 203–216.
AMO, A., MONTERO, J., BIGING, G., and CUTELLO, V. (2004), Fuzzy classification systems, Europ. J. Operat. Res.156, 459–507.
BEZDEK, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, New York 1981).
BEZDEK, J.C., and HARRIS, J.D. (1978), Fuzzy partitions and relations: An axiomatic basis for clustering, Fuzzy Sets and System 1, 111–127.
BINAGHI, E., BRIVIO, P.A., GHEZZI, P., and RAMPINI, A. (1999), A fuzzy set based accuracy assessment of soft classification, Pattern Recognition Lett. 20, 935–939.
CONGALTON, R.G., and BIGING, G. (1992), A pilot study evaluating ground reference data collection efforts for
use in forestry inventory, Photogrammetric Engin. Remote Sensing 58, 1669–1671.
CONGALTON, R.G., and GREEN, K. Assessing the Accuracy of Remote Sensed Data, Principles and Practices (Lewis Publishers, London 1999).
DRIESE, K.L., REINERS, W.A., LOVETT, G.M., and SIMKIN, S.M. (2004), A vegetation map for the Catskill Park,NY, derived from multi-temporal Landsat imagery and GIS data,Northeastern Naturalist 11, 421–442.
DUBOIS, D., and PRADE, H., Fuzzy sets and Systems, Theory and Applications (Academic Press, New York 1980).
DUBOIS, D., and PRADE, H. (1983), Ranking fuzzy numbers in the setting of possibility theory, Information Sci. 30,183–224.
FACCHINETTI, G., and RICCI, R.G. (2004), A characterization of a general class of ranking functions on triangular fuzzy numbers, Fuzzy Sets and Systems 146, 297–312.
FOODY, G.M. (1999), The continuum of classification fuzziness in thematics mapping, Photogrammetric Engin.Remote Sensing 65, 443–451.
GONZALEZ-PACHON, J., GOMEZ, D., MONTERO, J., and YAñEZ, J. (2003a), Soft dimension theory, Fuzzy Set and Systems 137, 137–149.
GONZALEZ-PACHON, J., GOMEZ, D., MONTERO, J., and YAÑEZ, J. (2003b), Searching for the dimension of binary valued preference relations, Internat. J. Approx. Reasoning 33, 133–157.
GOMEZ, D., MONTERO, J., YAñEZ, J., and POIDOMANI, C. (2007), A graph coloring algorithm approach for image
segmentation, Omega 35, 173–183.
GOMEZ, D., MONTERO, J., and YAñEZ, J. (2006), A coloring algorithm for image classification, Infor. Sci. 176,3645–3657.
GOMEZ, D., MONTERO, J., and LOPEZ, V., The role of fuzziness in decision making, In Fuzzy Logic: A Spectrum of
Applied and Theoretical Issues (eds. Ruan, D. et al.)(Springer 2008a) pp. 337–349.
GOMEZ, D., BIGING, G., and MONTERO, J. (2008b), Accuracy statistics for judging soft classification, Internat.J. Remote Sensing, 29, 693–709. DOI: 10.1080/01431160701311325.
KABVA, O., and SEIKKALA, S. (1994), On fuzzy metric spaces, Fuzzy Sets and Systems 12, 215–229.
KERRE, E.E., and NACHTEGAEL, M. Fuzzy Techniques in Image Processing (Physica-Verlag, Heidelberg 2000).
LABA, M., GREGORY, S.K., BRADEN, J., OGURCAK, D., HILL, E., FEGRAUS, E., FIORE, J., and DEGLORIA, S.D. (2002),
Conventional and fuzzy accuracy assessment of the New York Gap Analysis Project land cover map, Remote sensing of Environ. 81, 443–455.
MATSAKIS, P., ANDREFOUE¨T, S., and CAPOLSINI, P. (2000), Evaluation of fuzzy partitions, Remote Sensing of Environ. 74, 516–533.
MONTERO, J., Classifiers and decision makers. In Applied Computational Intelligence (eds. Ruan D. et al.)(World Scientific, Singapore 2004) pp. 19–24.
MONTERO, J., GOMEZ, D., and BUSTINCE, H. (2007), On the relevance of some families of fuzzy sets, Fuzzy Sets and Systems. 158, 2429–2442.
PAL, S.K., GOSH, A., and KUNDU, M.K., Soft Computing for Image Processing (Physica-Verlag, Heidelberg 2000).
PETRY, F.E., ROBINSON, V.B., and COBB, M.A., Fuzzy Modeling with Spatial Information for Geographic Problems (Springer, Berlin. 2005).
RUSPINI, E.H. (1969), A new approach to clustering, Inform. and Control 15, 22–32.
SAATY, T.L., Fundamentals of Decision Making with the Analytic Hierarchy Process (RWS Publications,Pittsburgh (1994), revised in 2000).
SEDANO, F., GOMEZ, D., GONG, P., and BIGING, G. (2008), Tree density estimation in a tropical woodland ecosystem with multiangular MISR and MODIS data, Remote Sensig Eviron., 112, 2523–2537.
WOODCOCK, C.E., and GOPAL, S. (2000), Fuzzy set theory and thematics maps, accuracy assessment and area estimation, Internat. J. Geograph. Inform Sci. 14, 153–172.
|Deposited On:||10 Sep 2012 13:52|
|Last Modified:||10 Sep 2012 13:52|
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