基于非负稀疏图的高光谱数据降维

为减少因大量的光谱信息带来的计算复杂及数据冗余带来的高光谱数据分类性能降低，该文提出一种非负稀疏图降维算法。首先，构建超完备块字典对高维高光谱数据进行非负稀疏表示。然后，根据块非负稀疏表示，分别构建内部非负稀疏图和惩罚非负稀疏图，基于单调递减函数定义边的权重以体现样本间的相似程度。最后，通过同时最大化异类和最小化同类非负稀疏重构样本间的距离，得到从高维到低维的最优映射关系，从而实现对高维高光谱数据的降维。AVIRIS 92AV3C高光谱数据上的实验结果表明，所提算法能以较少的训练样本获得较高的整体分类精度和Kappa系数。

Dimensionality Reduction of Hyperspectral Data Using Non-negative Sparsity Graph

In order to reduce the computation complexity resulted from large number of spectral information and to reduce the decline of classification performance resulted from data redundancy, a dimensionality reduction algorithm called non-negative sparse graph is proposed. At first, an over-complete block dictionary is constructed to realize the non-negative sparse representation of high-dimensional hyperspectral data. Then, according to the non-negative sparse representation, an inner non-negative sparsity graph and a penalty non-negative sparsity graph are built where the weights of edges are defined by a monotone decreasing function to embody the similarity degree of samples. At last, an optimal mapping from the high-dimensional space to a low-dimensional subspace can be obtained by simultaneously maximizing the distance between non-negative sparsity reconstruction samples of different classes and minimizing the distance between non-negative sparsity reconstruction samples of the same class, which makes the dimensionality reduction of high-dimensional hyperspectral data realized. Experimental results on AVIRIS 92AV3C hyperspectral data show that the proposed algorithm can obtain higher overall accuracy and Kappa coefficient with few training samples.