基于多观测向量的高光谱影像稀疏表达分类

针对传统的高光谱影像稀疏表达分类模型忽略像元间的内部结构关系且运算效率较低，提出多观测向量的稀疏表达模型来研究高光谱影像分类.该模型引入平衡参数来控制各权重系数向量的稀疏度，通过最小化L2范数约束的重构误差来求解所有测试像元的稀疏系数向量.基于两个高光谱数据集，对比5种常规分类器的分类结果来验证提出的方法.实验结果表明，多观测向量的稀疏表达分类模型在计算效率第二的同时能够得到最高分类精度.
Traditional sparse representation based classifiers ignore inter connections among pixels and have high computational complexity when applied in hyperspectral imagery (HSI) field. Therefore, a multiple measurement vectors based sparse representation classifier (MMV SRC) model is proposed to solve the above problems. The model introduces a balance parameter to control the sparsity of coefficient vectors, and estimates sparse coefficient vectors of all testing pixels by minimizing reconstruction errors using the L2 norm constraint. Experiments on two HSI datasets are implemented to test the performance of MMV SRC, and the results are compared with those of five state of the art classifiers. The results show that MMV SRC achieves best classification accuracies among all whereas taking the second shortest computational time