Comparative analysis of fuzzy approaches to remote sensing image classification

This paper compares four commonly used fuzzy analytical methods for remote sensing digital image classification, i.e. fuzzy c-means, semi-supervised fuzzy cluster labeling, fuzzy nearest neighbor, and object-oriented fuzzy classifiers. Merits and weak points of each method were examined through a case study with a multispectral high-resolution airborne digital image of urban settings. Results showed that the fuzzy labeling approach produced the highest quality, which was followed by the object-oriented fuzzy classifier. As the former combines merits of supervised and unsupervised classifications, the latter takes the full account of contextual and spatial features.