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Extended Multi-Level Logistic Model and SAR Image Segmentation

机译:扩展多级逻辑模型和SAR图像分段

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An extended multi-level logistic (EMLL) model and its parameter estimation method is proposed in this paper. The multi-level logistic (MLL) model is an extendedly used markov random field model. When using MLL model for label image, there is an underlying supposition that all boundaries have the same characters. This supposition is not always correct The proposed EMLL model solves the drawback of MLL model by associating different parameters with different boundaries. EMLL model has much more parameters than MLL model. A linear parameter estimation method for EMLL model is proposed and the experimental results for synthetic label images show that it performs well. We use EMLL model and its parameter estimation method in MAP segmentation of SAR images and the segmentation results show that EMLL model performs better than MLL model.
机译:本文提出了扩展的多级逻辑(EMLL)模型及其参数估计方法。 多级逻辑(MLL)模型是一个冗长使用的马尔可夫随机字段模型。 使用MLL模型的标签映像时,存在所有边界具有相同字符的底层假定。 该假设并不总是纠正,所提出的EMLL模型通过将不同的参数与不同的边界相关联来解决MLL模型的缺点。 EMLL模型的参数比MLL模型更多。 提出了一种用于EMLL模型的线性参数估计方法,合成标签图像的实验结果表明它表现良好。 我们在SAR图像的地图分割中使用EMLL模型及其参数估计方法,分段结果表明EMLL模型比MLL模型更好。

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