A Boosted Genetic Fuzzy Classifier for land cover classification of remote sensing imagery |
| |
| Authors: | DG StavrakoudisJB Theocharis GC Zalidis |
| |
| Institution: | a Aristotle University of Thessaloniki, Department of Electrical and Computer Engineering, Division of Electronics and Computer. Eng., 54124 Thessaloniki, Greeceb Aristotle University of Thessaloniki, Faculty of Agronomy, Lab of Applied Soil Science, 54124 Thessaloniki, Greece |
| |
| Abstract: | A Boosted Genetic Fuzzy Classifier (BGFC) is proposed in this paper, for land cover classification from multispectral images. The model comprises a set of fuzzy classification rules, which resemble the reasoning employed by humans. Fuzzy rules are generated in an iterative fashion, incrementally covering subspaces of the feature space, as directed by a boosting algorithm. Each rule is able to select the required features, further improving the interpretability of the obtained model. After the rule generation stage, a genetic tuning stage is employed, aiming at improving the cooperation among the fuzzy rules, thus increasing the classification performance attained after the first stage. The BGFC is tested using an IKONOS multispectral VHR image, in a lake-wetland ecosystem of international importance. For effective classification, we consider advanced feature sets, containing spectral and textural feature types. Comparative results with well-known classifiers, commonly employed in remote sensing tasks, indicate that the proposed system is able to handle multi-dimensional feature spaces more efficiently, effectively exploiting information from different feature sources. |
| |
| Keywords: | AdaBoost Genetic fuzzy rule-based classification systems (GFRBCS) Local feature selection Textural and spatial features Multispectral image classification |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
