Median-mean line based collaborative representation for PolSAR terrain classification
Median-mean line based collaborative representation for PolSAR terrain classification
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DOI：
10.1016/j.ejrs.2022.01.011
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
M. Imani
中科院分区：
文献类型：
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作者：
M. Imani
提出了一种基于协同表示的极化合成孔径雷达(PolSAR)数据分类方法。虽然CR能够很好地平滑PolSAR数据并去除相干斑噪声，但它可能会降低非均匀区域中的类边界。针对这一困难，提出了一种考虑边缘信息的加权CR算法。此外，为了进一步利用上下文信息，CR的剩余项被平滑，而不匹配项被最小化。此外，通过对均值和中值进行内插或外推，使用中值-均值线度量来降低离群点影响。该方法被称为基于中值均值线的认知无线电(MMLCR)，在训练样本数量有限的情况下，可以获得更好的PolSAR分类结果。例如，对于包含15个类的Flevoland数据集，在每个类仅使用10个训练样本的情况下，总体分类准确率达到94.79%。
A collaborative representation (CR) based method is proposed for polarimetric synthetic aperture radar (PolSAR) data classification in this work. Although CR can well smooth the PolSAR data and remove the speckle noise but it may degrade the class boundaries in heterogeneous regions. To deal with this difficulty, a weighted CR with considering the edge information is proposed. In addition, to further utilize the contextual information, the residual terms of CR are smoothed while the misfitting terms are minimized. Moreover, the median-mean line metric is used to degrade the outlier effects with involving interpolation or extrapolation of mean and median values. The proposed method called median-mean line based CR (MMLCR) leads to superior PolSAR classification results particularly when a limited number of training samples is available. For example, 94.79% overall classification accuracy is achieved for classification of the Flevoland dataset containing 15 classes with just using 10 training samples per class.