论文标题
基于超级像素的图形拉普拉斯正则化，用于稀疏的高光谱脉络
Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing
论文作者
论文摘要
提出了一种有效的空间正则方法，该方法使用超像素分割和图形拉普拉斯正则化是针对稀疏的高光谱杂音方法提出的。由于可能在均匀区域中找到频谱相似的像素，因此我们使用超像素分割算法来考虑图像边界来提取均匀区域。我们首先提取均匀的区域（称为Superpixels），然后通过在每个Superpixel中选择$ k $ - neart像素来构建每个超级像素中的加权图。图中的每个节点代表像素的光谱，边缘连接了超像素内部的类似像素。使用图Laplacian正则化研究了空间相似性。使用促进标准的加权稀疏性提供了丰度基质的稀疏性正则化。对模拟和真实数据集的实验结果表明，所提出的算法优于文献中众所周知的算法。
An efficient spatial regularization method using superpixel segmentation and graph Laplacian regularization is proposed for sparse hyperspectral unmixing method. Since it is likely to find spectrally similar pixels in a homogeneous region, we use a superpixel segmentation algorithm to extract the homogeneous regions by considering the image boundaries. We first extract the homogeneous regions, which are called superpixels, then a weighted graph in each superpixel is constructed by selecting $K$-nearest pixels in each superpixel. Each node in the graph represents the spectrum of a pixel and edges connect the similar pixels inside the superpixel. The spatial similarity is investigated using graph Laplacian regularization. Sparsity regularization for abundance matrix is provided using a weighted sparsity promoting norm. Experimental results on simulated and real data sets show the superiority of the proposed algorithm over the well-known algorithms in the literature.
