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- W2772137563 abstract "Classification is one of the most important applications and also key technology of Polarimetric SAR image interpretation, which mainly includes feature extraction and optimization of classifiers. For high resolution Polarimetric SAR images, the fine description and accurate classification becomes increasingly complex and difficult with a single feature or classifier. Thus, the selective ensemble learning theory is introduced into PolSAR image classification. This paper presents a polarimetric SAR image classification method based on selective ensemble learning via fuzzy c-mean clustering. Firstly, train a plurality of basic classifiers for PolSAR image classification using the training samples and the effective features, next cluster the classifiers utilizing fuzzy C-means (FCM) clustering, select some clustering centers and integrate the selected classifiers to acquire more accurate results. L-band fully polarimetric EMISAR data of Foulum area of Denmark are utilized to demonstrate the feasibility of the method, and the preliminary results shows the accuracy can be improved." @default.
- W2772137563 created "2017-12-22" @default.
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- W2772137563 date "2017-07-01" @default.
- W2772137563 modified "2023-09-26" @default.
- W2772137563 title "Polarimetric SAR images classification via FCM-based selective ensemble learning" @default.
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- W2772137563 doi "https://doi.org/10.1109/igarss.2017.8128032" @default.
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