130 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 50, NO. 1, JANUARY 2012 A Genetic Fuzzy-Rule-Based Classifier for Land Cover Classification From Hyperspectral Imagery Dimitris G. Stavrakoudis, Georgia N. Galidaki, Ioannis Z. Gitas, and John B. Theocharis, Member, IEEE Abstract—This paper proposes the use of a genetic fuzzy-rule- based classification system for land cover classification from hyperspectral images. The proposed classifier, namely, Feature Selective Linguistic Classifier, is constructed through a three-stage learning process. The first stage produces a preliminary fuzzy rule base in an iterative fashion. During this stage, a local feature selec- tion scheme is employed, designed to guide the genetic evolution, through the evaluation of deterministic information about the rel- evance of each feature with respect to its classification ability. The structure of the model is then simplified in a subsequent postpro- cessing stage. The performance of the classifier is finally optimized through a genetic tuning stage. An extensive comparative analysis, using an Earth Observing-1 Hyperion satellite image, highlights the quality advantages of the proposed system, when compared with nonfuzzy classifiers, commonly employed in hyperspectral classification tasks. Index Terms—AdaBoost, evolutionary algorithms (EAs), genetic fuzzy-rule-based classification systems (FRBCSs) (GFRBCSs), genetic tuning, hyperspectral image classification, local feature selection, remote sensing. I. I NTRODUCTION T HE GROWING development and availability of hyper- spectral sensor technologies over the past two decades have provided new possibilities in improving the accuracy of land cover classification from satellite images. Hyperspectral sensors collect several (typically 200 or more) narrow spectral bands from the visible to the short-wave infrared portions of the electromagnetic spectrum, providing an almost continuous spectral reflectance signature. It has been reported that hyper- spectral data are capable of producing both genus- and species- level classifications, whereas multispectral data are well suited for genus-level classifications [1]. In particular, in land cover classification of forests, where typically different species of the same genus coexist, it has been shown that hyperspectral Manuscript received June 17, 2010; revised February 22, 2011; accepted May 22, 2011. Date of publication July 29, 2011; date of current version December 23, 2011. D. G. Stavrakoudis and J. B. Theocharis are with the Department of Electrical and Computer Engineering, Division of Electronics and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece (e-mail: jstavrak@auth.gr; theochar@eng.auth.gr). G. N. Galidaki was with Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece. She is now with the Biology Depart- ment, University of Trieste, 34127 Trieste, Italy (e-mail: galidaki@for.auth.gr). I. Z. Gitas is with the Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece (e-mail: igitas@for.auth.gr). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2011.2159613 satellite imagery can significantly increase the classification accuracy [2]. The high number of features encountered in remote-sensing classification tasks from hyperspectral images has rendered the use of simple statistical classifiers—commonly employed in multispectral image classification—ineffective. Therefore, more sophisticated classifiers must be considered in order to exploit the rich spectral information offered by hyperspectral data. In this direction, various methods from the field of pattern recognition and artificial intelligence have been proposed for handling high-dimensional remote-sensing data: neural net- works (NNs) [3], fuzzy clustering algorithms [4], decision trees [5], genetic algorithms (GAs) [6], kernel-based techniques [7], and combinations of them [8], [9]. In particular, support vector machines (SVMs) have been extensively used because of their good classification performance in high-dimensional data sets [10]–[12]. These proposals focus primarily on the accuracy of the resulting thematic map, and they commonly apply a feature selection preprocessing step in order to reduce the high- dimensional feature space [6], [12]. Another approach taken by the respective research commu- nity focuses on the interpretability of the considered classifi- cation model. Indeed, most of the aforementioned classifiers act as black boxes, providing only the class label for each pixel. However, in many cases, it is desirable to derive a better understating of the underlying physical relations of the classi- fication problem, which is useful from an operational remote- sensing perspective. Toward this direction, the use of rule-based classifiers [13] and decision trees [14]–[16] has been consid- ered. Because of the inherent interpretability of these methods, an analyst can examine the resulting model and identify the important factors that distinguish classes from one another. In the context of interpretable classification models, fuzzy- rule-based classification systems (FRBCSs) have arguably the most intuitive representation, represented by simple rules with a linguistic meaning. Moreover, the fuzzy partition of the feature space can describe the naturally overlapping spectral signatures of closely related vegetation species more accurately as com- pared to crisp (nonfuzzy) classifiers. However, their application to remote-sensing classification tasks has been rather limited, with the existing proposals employing simple deterministic learning methods and applying them to multispectral data only [17]–[20]. The generation of FRBCSs for data sets comprising a high number of features is a challenging task, since the number of possible fuzzy rules increases exponentially with the increase of the feature space. In the latter direction, the enhanced search capabilities of GAs [21] have been extensively used in the derivation of fuzzy-rule-based systems (FRBSs), giving rise to 0196-2892/$26.00 © 2011 IEEE