Fuzzy rule-based classification of remotely sensed imagery

The purpose of this paper is to investigate the applicability of fuzzy rule-based modeling to classify a LANDSAT TM scene from 1984 of an area located in the south of Germany. Both a land cover map with four different categories and an image depicting the degree of ambiguity of the classification for each pixel is the expected output. The fuzzy classification algorithm will use a rule system derived from a training set using simulated annealing as an optimization algorithm. The results are then validated and compared with a common classification method in order to judge the effectiveness of the proposed technique. It will also be shown that the proposed method with only nine rules for four different land cover classes performs slightly better than the maximum likelihood classifier (MLC). For error assessment, the traditional error matrix and fuzzy operators have been used.

[1]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[2]  Ralph Bernstein,et al.  Gaussian Maximum Likelihood and Contextual Classification Algorithms for Multicrop Classification , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[3]  C. Woodcock,et al.  Theory and methods for accuracy assessment of thematic maps using fuzzy sets , 1994 .

[4]  Elisabetta Binaghi,et al.  A fuzzy set-based accuracy assessment of soft classification , 1999, Pattern Recognit. Lett..

[5]  F. Wang.  Improving remote sensing image analysis through fuzzy information representation , 1990 .

[6]  Ursula C. Benz,et al.  Measures of classification accuracy based on fuzzy similarity , 2000, IEEE Trans. Geosci. Remote. Sens..

[7]  G. Foody,et al.  A fuzzy classification of sub-urban land cover from remotely sensed imagery , 1998 .

[8]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[9]  Kanti V. Mardia,et al.  Spatial Classification Using Fuzzy Membership Models , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  G. M. Foody.  The Continuum of Classification Fuzziness in Thematic Mapping , 1999 .

[11]  Giles M. Foody,et al.  Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data , 1996 .

[12]  Elisabetta Binaghi,et al.  Fuzzy decision making in the classification of multisource remote sensing data , 1993 .

[13]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[14]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[15]  Mohan Trivedi,et al.  Segmentation of a Thematic Mapper Image Using the Fuzzy c-Means Clusterng Algorthm , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Robert Jeansoulin,et al.  Multitemporal Segmentation by Means of Fuzzy Sets , 1981 .

[17]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[18]  Fangju Wang,et al.  Fuzzy supervised classification of remote sensing images , 1990 .

[19]  Emile H. L. Aarts,et al.  Simulated annealing and Boltzmann machines - a stochastic approach to combinatorial optimization and neural computing , 1990, Wiley-Interscience series in discrete mathematics and optimization.

[20]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[21]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.