基于二次空间处理的联合稀疏表示高光谱图像分类

针对许多应用于高光谱图像分类的传统算法存在的分类精度低、光谱和空间信息利用不充分的问题，提出了一种基于二次空间处理的联合稀疏表示高光谱图像分类算法。在字典训练之前提取形态学特征，和光谱特征共同构建初始字典，以达到更快训练出较高质量的字典原子的目的。为了充分利用空间信息，首先通过超像素分割获取边缘信息，然后在超像素边缘和固定邻域双重约束下通过权值计算自适应选择邻域原子，实现空间信息的二次利用。在两个常用数据集上进行仿真实验，证明了本文所提算法可有效提升分类精度。 

Joint sparse representation of hyperspectral image classification based on quadratic space processing

In view of the problems of low classification accuracy and insufficient utilization of spectral and spatial information in many traditional algorithms applied to hyperspectral image classification, a joint sparse representation hyperspectral image classification algorithm based on quadratic spatial processing was proposed. The morphological features are extracted before the dictionary training, and the initial dictionary is constructed together with the spectral features to achieve the purpose of training higher quality dictionary atoms faster. In order to make full use of spatial information, obtaining edge information through superpixel segmentation firstly, and then adaptively selects neighboring atoms through weight calculation under the dual constraints of superpixel edges and fixed neighborhoods to realize the secondary utilization of spatial information. Simulation experiments on two commonly used datasets prove that the algorithm proposed in this paper can effectively improve the classification accuracy.