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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Fast Semisupervised Classification Using Histogram-Based Density Estimation for Large-Scale Polarimetric SAR Data
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Fast Semisupervised Classification Using Histogram-Based Density Estimation for Large-Scale Polarimetric SAR Data

机译：大规模极化SAR数据的基于直方图密度估计的快速半监督分类

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摘要

In order to obtain high classification accuracy and reduce time consumption for large-scale polarimetric synthetic-aperture radar (PolSAR) data. In this letter, we propose a fast semisupervised classification algorithm using histogram-based density estimation (called FSHDE). First, a noniterative collaborative training using our proposed Wishart-clustering selection strategy is designed to expand the labeled sample set from unlabeled samples. Second, a fast feature mapping based on histogram density estimation is employed to reliably capture the interaction of nonlinear features. Third, submodular optimization is used to select optimal subspace features to reduce feature correlation. Experimental results on synthetic and real PolSAR data indicate that FSHDE greatly reduces the time consumption and improves the accuracy for terrain classification compared with the state-of-the-art methods.
机译：为了获得高分类精度并减少大型极化合成孔径雷达（PolSAR）数据的时间消耗。在这封信中，我们提出了一种使用基于直方图的密度估计（称为FSHDE）的快速半监督分类算法。首先，使用我们提出的Wishart聚类选择策略进行非迭代协作培训，旨在从未标记的样本中扩展标记的样本集。其次，基于直方图密度估计的快速特征映射被用来可靠地捕获非线性特征的相互作用。第三，子模优化用于选择最佳子空间特征以减少特征相关性。综合和实际PolSAR数据的实验结果表明，与最新方法相比，FSHDE大大减少了时间消耗并提高了地形分类的准确性。

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