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A High-Efficiency Automatic U-Distribution Segmentation Algorithm for PolSAR Images
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DOI：10.1109/LGRS.2018.2881188.png)
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A fully automatic, non-Gaussian, and contextual clustering algorithm for segmentation of polarimetric synthetic aperture radar (SAR) images has been previously presented by Doulgeris. It achieved good results for both simulated and actual data sets. However, the long computation time was its main drawback. This letter discusses modifications to improve computational efficiency. The primary speed issues were rooted in the complicated probability density function (PDF) of the adopted model, for which evaluating the posterior probability of samples and estimating the parameters were both very time-consuming. We investigate the model parameters, reparametrize the model, and introduce lookup tables to speed up the processing chain. The new strategy speeds up both PDF evaluation and parameter estimation while maintaining the exactly similar visual results and now makes advanced non-Gaussian SAR image analysis a practical alternative.
Keyword:
Efficiency improvement
polarimetric synthetic aperture radar (PolSAR)
segmentation
statistic model
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Semisupervised Classification of Polarimetric SAR Image via Superpixel Restrained Deep Neural Network基于超像素约束深度神经网络的极化SAR图像半监督分类
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分享收藏分享收藏An Automatic U-Distribution and Markov Random Field Segmentation Algorithm for PolSAR Images极化sar图像的自动U分布和马尔可夫随机场分割算法
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