遥感影像的半监督判别局部排列降维

Dimensionality Reduction of Remote Sensing Image Using Semi-Supervised Discriminative Locality Alignment

针对遥感影像数据具有的高维数和少量已标记样本的特性,提出一种基于图的半监督判别局部排列降维方法.首先,针对全部已标记和未标记样本数据构造相似图和惩罚图.然后,基于同类近邻点的分散度最小且不同类近邻点的分散度最大的原则,分别确立相似图和惩罚图上的优化目标.最后,通过同时优化这两种图上的目标函数,得到从高维到低维的最优映射关系,从而达到对高维遥感影像数据维数约简的目的.ROSIS高光谱数据上的实验结果表明,所提算法能够有效提高高维遥感影像的总体精度和Kappa系数.

Aiming at remote sensing image data having properties of high-dimension and small amount of labeled samples,a dimensionality reduction algorithm called semi-supervised discriminative locality alignment based on graph is proposed. At first, a similarity graph and a penalty graph are constructed according to all labelled and unlabelled samples. Then,based on the principle that the dispersion between neighbours of the same class is minimum and that the dispersion between neighbours of different class is maximum,optimization goals on the similarity graph and on the penalty graph are respectively determined.At last,an optimal map- ping from the high-dimensional space to a low-dimensional subspace can be obtained by simultaneously optimizing the two objective functions, which makes the dimensionality reduction of high-dimensional remote sensing images realized. Experimental results on ROSIS hepectml data show that the proposed algorithm can effectively improved the overall accuracy and Kappa coefficient of high-dimensional remote sensing images. Koy words： semi-supervised;discriminative locality alignrnent;graph;dimensionality reduction;remote sensing image