Improvement of land-use classification using object-oriented and fuzzy logic approach

Traditional classification approaches are all pixel-based and do not utilize the spatial and context information of an object and its surroundings, which has potential to further enhance digital image classification. Instead of pixel-based, pixels groupings and object segmentation offers more innovative techniques to image classification. In this study, land cover types in the Klang valley, Malaysia were analyzed to compare classification accuracy between the pixel-based and the object-oriented image classification approaches. Landsat 7 ETM+ with six spectral bands was used for the land cover classification. In the pixel-based image classification, supervised classification was performed using the maximum likelihood classifier. On the other hand, the object-oriented image classification was performed using the combination of object segmentation using fuzzy dimension techniques. The selected parameters for image segmentation were: scale parameter 15, homogeneity composition criterion (color 0.7 and shape 0.3), shape criterion (smoothness 0.9 and compactness 0.1). Fuzzy dimension functions were devised to classify the segmented image objects. The classification results showed that the object-oriented cum fuzzy logic approach was superior to that of the pixel-based supervised classification. The former has achieved higher overall, producer and user accuracies for most of the land cover classes compared to those of the latter. In addition, the accuracy of the former has met the requirements of international standard for digital mapping with overall accuracy exceeding 85%; Kappa value above 0.85 while accuracy differences among the classes were kept minimal.