基于空谱字典的加权联合稀疏表示高光谱图像分类  

Weighted Joint Sparse Representation Hyperspectral Image Classification Based on Spatial-Spectral Dictionary

作　　者：陈善学[1,2] 何宇峰 Chen Shanxue;He Yufeng(Chongqing Key Laboratory of Mobile Communications Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学移动通信技术重庆市重点实验室,重庆400065 [2]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《光学学报》2023年第1期58-68,共11页Acta Optica Sinica

摘　　要：稀疏表示广泛用于高光谱图像分类任务中。针对字典原子空间信息和光谱信息未得到充分利用的问题,提出了基于空谱字典的加权联合稀疏表示高光谱图像分类算法。计算测试像元与字典原子的空谱联合距离,选择相似度最高的K个字典原子,并将被选择字典原子的超像素邻域扩充到新的字典中,形成空谱字典。在联合稀疏模型中,对测试像元的超像素邻域像元使用不同的权重,在空谱字典上构建加权稀疏表示模型。基于所选的两个高光谱数据集的实验证明所提算法能够有效地提高分类精度。Objective Hyperspectral image classification aims to assign feature labels to each image element in images. Nowadays,several classification techniques are applied in hyperspectral classification, such as support vector machines(SVMs),polynomial logistic regression, and neural networks. In recent years, sparse representation has proven to be a powerful tool for solving problems such as face recognition and image super-resolution. The basic assumption of sparse representation is that if a class has enough training samples, the test samples belonging to this class can be represented by using a linear combination of the training samples from this class. Sparse representation classification obtains the sparse representation parameters by the sparse representation of the test samples and calculates the reconstructed residuals for each class of the training samples, which thus determines the class of the test samples. The sparse representation usually pays more attention to the spatial information of the neighborhood of the test image elements and ignores the spatial information of dictionary atoms. The proposed weighted joint sparse representation hyperspectral image classification algorithm based on the spatial-spectral dictionary(SSD-WJSRC) addresses the problem that the spatial-spectral information of dictionary atoms is underutilized.Methods SSD-WJSRC algorithm makes full use of the spatial-spectral information of dictionary atoms. Firstly, the superpixel segmentation is performed by using the entropy rate superpixel segmentation(ERS) algorithm on the principal component image to obtain the superpixel segmentation map. Secondly, the spatial-spectral joint distance between the test image elements and the dictionary atoms is calculated, and the spatial-spectral joint distance is jointly determined by the spatial distance and the spectral angle distance. Then, image elements are added in the superpixel neighborhood corresponding to the first K dictionary atoms to the spatial-spectral dictionary as sub-dictionaries. Me

关 键 词：图像处理 高光谱图像分类 空谱字典 超像素 稀疏表示 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]