Unsupervised classification based on fuzzy c-means with uncertainty analysis

Fuzzy c-means (FCM) is a widely used unsupervised classifier for remote sensing images. This letter presents an uncertainty analysis-based FCM (UAFCM) classification method. The uncertainty in this letter refers to the discriminative ability of class attributes in fuzzy classification on a per-pixel basis. UAFCM is performed by analysing the uncertainty in FCM classification result and reclassifying the pixels with large uncertainty. Specifically, the uncertainty in FCM classification is measured by entropy and a proposed square error-based criterion. A threshold value is then determined to recognize the pixels with large uncertainty, which are reclassified with spatial connectivity subsequently. Experiments on three remote sensing images show that the proposed UAFCM consistently obtains more accurate classification results than does FCM, and hence provides an effective new unsupervised classification method for remote sensing images.