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.

在这项工作中，提出了一种基于协同表示（CR）的极化合成孔径雷达（PolSAR）数据分类方法。虽然 CR 可以很好地平滑 PolSAR 数据并去除斑点噪声，但它可能会降低异构区域中的类边界。为了解决这个难题，提出了一种考虑边缘信息的加权CR。此外，为了进一步利用上下文信息，对 CR 的残差项进行平滑处理，同时最小化失配项。此外，中值-均值线度量用于通过涉及平均值和中值的插值或外推来降低异常值影响。所提出的称为基于中值平均线的 CR (MMLCR) 的方法会导致出色的 PolSAR 分类结果，特别是在可用的训练样本数量有限的情况下。例如，