Abstract
The use of contextual information is beneficial to improve both the accuracy and reliability of image classification. Based on the robust fuzzy ${c}$ -means (RFCM) clustering method and an adaptive Markov random field model, this letter proposes a contextual ${H}/{\bar {\alpha }}$ classifier for polarimetric synthetic aperture radar images. At each iterative step of RFCM clustering, the prior probability extracted from the local neighborhood is combined with the fuzzy membership derived from inherent polarimetric characteristics, thus the enhanced fuzzy membership is more reliable. In addition, an adaptive smoothing factor is proposed for use during contextual information retrieval, which can prevent oversmoothing and preserve the local spatial details. The experimental results implemented using AIRSAR and ESAR L-band data validate the efficacy of the proposed method. Compared with the iterated Wishart classifier and fuzzy ${H}/{\bar {\alpha }}$ classifier, the proposed method significantly improves the classification accuracy, with less noise and increased preservation of details.
Published Version
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