A fuzzy classification approach for high-resolution multispectral data over urban areas

Abstract

The usefulness of high-resolution satellite imagery for classification of urban and suburban scenes is investigated, and a technique to improve the accuracy of the classification is proposed. The Ikonos commercial remote sensing satellite acquired the imagery (panchromatic and multispectral) used for this study. Both multispectral and pan-sharpened multispectral images are classified using maximum likelihood classification. The overall classification accuracy for both data sets is approximately 81%, however there are significant numbers of misclassifications between the spectrally similar road and building classes and the tree and grass classes. The confusion between the building and road classes was about 22%, and the confusion between the grass and tree classes was about 13%. To decrease the number of misclassifications between the previously mentioned classes, a hierarchical fuzzy classification technique is proposed. The fuzzy classifier makes use of spatial features extracted from the panchromatic data, pan-sharpened multispectral data, and a classification image, generated using maximum likelihood classification. A number of different texture features and directional pixel length-width contextual features are extracted from the panchromatic image data. A fuzzy logic rule based classifier is used in conjunction with these spatial features to perform the classification. The proposed approach decreases the number of misclassifications between the road and building classes and the number of misclassifications between the grass and tree classes raising the overall accuracy to 88%.