DocumentCode :
593938
Title :
Gene Expression Programming-Fuzzy Logic Method for Crop Type Classification
Author :
Omkar, S.N. ; Ramaswamy, Nirmal ; Senthilnath, J. ; Bharath, S. ; Anuradha, N.S.
Author_Institution :
Dept. of Aerosp. Eng., Indian Inst. of Sci., Bangalore, India
fYear :
2012
fDate :
25-28 Aug. 2012
Firstpage :
136
Lastpage :
139
Abstract :
Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multi-class crop classification using Multispectral satellite image is proposed. the purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. the capabilities of GEP as a classifier is also studied. the proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. from the results we can conclude that the proposed method is effective for classification.
Keywords :
Bayes methods; crops; fuzzy set theory; genetic algorithms; geophysical image processing; image classification; land use planning; maximum likelihood estimation; performance evaluation; production engineering computing; productivity; remote sensing; Bayesian classifier; GEP; agricultural crops; agricultural productivity improvement; crop type classification; cultivation planning; fuzzy logic method; fuzzy membership functions; gene expression programming; maximum likelihood classifier; multiclass crop classification; multispectral satellite image; optimal fertile land usage; performance evaluation; remote sensing data; Accuracy; Agriculture; Biological cells; Classification algorithms; Fuzzy logic; Gene expression; Programming; crop classification; fuzzy logic; gene expression programming;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Genetic and Evolutionary Computing (ICGEC), 2012 Sixth International Conference on
Conference_Location :
Kitakushu
Print_ISBN :
978-1-4673-2138-9
Type :
conf
DOI :
10.1109/ICGEC.2012.97
Filename :
6457194
Link To Document :
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