基于稀疏表示模型和自回归模型的高光谱分类

针对高光谱分类中对光谱信息和空间信息利用不足的问题,提出了一种基于稀疏表示模型和自回归模型相结合的分类算法。该算法利用稀疏表示模型和自回归模型,设计联合字典:在光谱维上,利用稀疏表示模型将高光谱的每个光谱向量表示为字典中训练样本的稀疏线性组合;在空间维上,利用自回归模型对每个光谱向量的8邻域进行约束。针对不同样本分别构造一个字典,在减少计算量的同时减小重构误差,最后在最小重构误差和邻域相关性的约束下求解稀疏表示问题,以最小重构误差为准则实现高光谱数据的分类。仿真结果表明,该方法能够有效地提高高光谱数据的分类精度。

Hyper-Spectrum Classification Based on Sparse Representation Model and Auto-Regressive Model

A novel classification approach based on sparse representation model and auto-regressive model is presented to deal with spectral and spatial information underutilization effectively for hyper-spectrum classification. The combination dictionary is designed using sparse representation model and auto-regressive model. Sparse representation model is used to represent every spectral vector as sparse linear combination of the training samples on spectral dimension; auto-regressive model is added to constrain every spectral vector by its eight neighborhoods on spatial dimension. A new dictionary is constructed for every class to reduce the computation and reconstruction error. At last, the sparse problem is recovered by solving a constrained optimization of minimum reconstruction error and neighboring relativity. The classification of hyper-spectral image is determined by computing the minimum reconstruction error of testing samples and training samples. Simulation results show that the method improves the classification accuracy.