基于空间约束加权条件稀疏表示高光谱图像分类

为了充分利用稀疏表示分类信息和高光谱图像的空间信息,提出结合马尔可夫随机场的加权条件稀疏表示高光谱图像分类算法。该算法对稀疏表示分解后的残差向量建立条件稀疏表示模型,在计算残差向量的类别归属时引入频段方差信息;利用光谱信息散度从信息熵的角度挖掘重构光谱中的类别鉴定信息;在期望最大化算法模型中,将条件稀疏模型与光谱信息散度模型相结合,使算法具备迭代自更新的能力;将马尔可夫随机场引入加权条件稀疏表示算法,在算法时间复杂度不变的情况下，对高光谱图像的空间信息予以提取。仿真结果表明,该算法能够有效地提高分类精度,且在不同试验数据下具备良好的稳定性。

Spatial correlation constrained weighted conditional sparse representation for hyperspectral image classification

To take full advantage of the information of the sparse representation classification algorithm and the spatial information of the hyperspectral image, a weighted conditional sparse representation combined with the Markov random field (MRF) algorithm is proposed. Firstly, a conditional sparse representation model is built for the residual vectors, which introduces the frequency band variance information into the problem of calculating the reconstructed error. Then the spectral information divergence is used to take advantage of the information of the reconstructed spectrum from the perspective of information entropy. The conditional sparse representation model is weighted by the spectral information divergence in the expectation maximization algorithm, which makes the proposed algorithm have the ability of self renew. On the condition of the same convergence rate, the MRF is introduced into the weighted conditional sparse representation algorithm to utilize the spatial information of the hyperspectral image. Simulation results show that the propose method can effectively improve the classification accuracy, and has good stability in different experimental data.