基于独立空谱残差融合的联合稀疏表示高光谱图像分类

Joint Sparse Representation Hyperspectral Image Classification Based on Independent Space-spectrum Residual Fusion

针对高光谱图像分类中存在的空间信息与光谱信息融合问题,提出一种基于独立空谱残差融合的联合稀疏表示高光谱图像分类算法。使用类独立的光谱角初分类图像,获得像元初始标记后按特定条件进行筛选再构造像元邻域空间。提取图像的全局空间信息,并将其引入到空谱联合稀疏表示模型中,使用单独的光谱信息字典与空间信息字典分别进行图像光谱与空间的联合稀疏表示残差计算。在此基础上,使用残差融合算法确定图像类别。实验结果表明,相对SVM、KNN等算法,该算法能够提升高光谱图像的分类精度,且分类结果更稳定。

Aiming at the problem of spatial information and spectral information fusion in hyperspectral image classification,a joint sparse representation hyperspectral image classification algorithm based on independent space-spectrum residual fusion is proposed.Class-independent spectral angles are used to classify the images,and the initial labels of pixels are obtained,then the neighborhood space of pixels is constructed according to the specific conditions.The global spatial information of the whole image is extracted and introduced into the joint sparse representation model of space-spectrum.The joint sparse representation residual of image spectrum and space is calculated by using a separate spectral information dictionary and a spatial information dictionary respectively.On this basis,the residual fusion algorithm is used to determine image classes.Experimental results show that compared with SVM,KNN and other algorithms,this algorithm can improve the classification accuracy of spectral images and the classification results are more stable.