Sparse Representation-Based Nearest Neighbor Classifiers for Hyperspectral Imagery
Sparse Representation-Based Nearest Neighbor Classifiers for Hyperspectral Imagery
复制标题
基于稀疏表示的高光谱图像最近邻分类器
DOI：
10.1109/lgrs.2015.2481181
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发表时间：
2015-12-01
影响因子：
4.8
通讯作者：
Du, Qian
中科院分区：
文献类型：
--
作者：
Zou, Jinyi;Li, Wei;Du, Qian
在这封信中，提出了一种基于稀疏表示的最近邻（SRNN）分类器。与传统的k-最近邻（NN）分类器采用欧氏距离作为相似性度量不同，SRNN考虑稀疏系数来确定测试样本的标签，因为稀疏系数可以反映数据之间的相似性并提供更多的区分信息。局部SRNN（LSRNN）分类器也提出了利用类特定的稀疏系数，以提高性能。此外，由于事实上，相邻像素往往属于同一类的概率很高，空间联合版本的LSRNN，称为JSRNN，开发进一步改善LSRNN。建议的SRNN，LSRNN，JSRNN已经验证了几个高光谱遥感图像数据集。实验结果表明，与传统的k-NN分类器、基于局部均值的NN（LMNN）分类器和基于残差的稀疏表示分类器相比，该分类器提高了分类精度.
In this letter, a sparse representation-based nearest neighbor (SRNN) classifier is proposed. Unlike the traditional k-nearest neighbor (NN) classifier that employs the Euclidean distance as similarity metric, the proposed SRNN considers sparse coefficients to determine the label of testing samples, since sparse coefficients can reflect the similarity between data and provide more discriminative information. A local SRNN (LSRNN) classifier is also proposed to utilize class-specific sparse coefficients to improve the performance. Furthermore, due to the fact that neighboring pixels tend to belong to the same class with high probability, a spatially joint version of LSRNN, called JSRNN, is developed to further improve LSRNN. The proposed SRNN, LSRNN, and JSRNN have been validated on several hyperspectral remote sensing image data sets. Experimental results demonstrate that the proposed classifiers increase the classification accuracy compared with the traditional k-NN, local mean-based NN (LMNN) classifiers, and original sparse representation classifiers using representation residuals.