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Proceedings Paper

Accurate crop classification using hierarchical genetic fuzzy rule-based systems
Author(s): Charalampos A. Topaloglou; Stelios K. Mylonas; Dimitris G. Stavrakoudis; Paris A. Mastorocostas; John B. Theocharis
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Paper Abstract

This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC’s model comprises a small set of simple IF–THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

Paper Details

Date Published: 21 October 2014
PDF: 12 pages
Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92391G (21 October 2014); doi: 10.1117/12.2067410
Show Author Affiliations
Charalampos A. Topaloglou, Aristotle Univ. of Thessaloniki (Greece)
Stelios K. Mylonas, Aristotle Univ. of Thessaloniki (Greece)
Dimitris G. Stavrakoudis, Aristotle Univ. of Thessaloniki (Greece)
Paris A. Mastorocostas, Technological Educational Institution of Serres (Greece)
John B. Theocharis, Aristotle Univ. of Thessaloniki (Greece)

Published in SPIE Proceedings Vol. 9239:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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