Superpixel Nonlocal Weighting Joint Sparse Representation for Hyperspectral Image Classification
Superpixel Nonlocal Weighting Joint Sparse Representation for Hyperspectral Image Classification
复制标题
高光谱图像分类的超像素非局部加权联合稀疏表示
DOI：
10.3390/rs14092125
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发表时间：
2022-04
期刊：
影响因子：
5
通讯作者：
Yanjuan Yao
中科院分区：
文献类型：
--
作者：
Aizhu Zhang;Zhaojie Pan;Hang Fu;Genyun Sun;Jun Rong;Jinchang Ren;Zhong-Ping Jiang;Yanjuan Yao
联合稀疏表示分类（JSRC）是一种具有代表性的高光谱图像光谱-空间分类器。然而，JSRC是不合适的高度异质性的地区，由于空间信息是从一个固定大小的街区，这往往是无法符合自然不规则的土地覆盖结构提取。为了解决这个问题，一个基于超像素的JSRC与非局部加权，即，提出了一种基于超像素的非局部加权JSRC（SNLW-JSRC）。在SNLW-JSRC中，首先基于熵率分割方法构建HSI的超像素表示。该策略形成具有自然不规则结构的同质邻域，并在空间信息提取过程中避免包含来自不同类别的像素。然后，基于超像元的非局部加权（SNLW）方案被建立来基于其结构和光谱信息对超像元进行加权。以这种方式，一个特定相邻像素的权重由相邻像素与中心测试像素之间的局部结构相似性来确定。然后，使用所获得的局部权重来生成每个超像素的加权平均数据。最后，使用JSRC产生超像素级分类。这加快了稀疏表示的速度，并使空间内容更加集中和紧凑。为了验证所提出的SNLW-JSRC方法，我们在四个基准高光谱数据集上进行了实验，即Indian Pines，Pavia University，Salinas和DFC 2013。实验结果表明，SNLW-JSRC算法的分类效果优于其他四种SRC算法和经典的支持向量机算法。此外，SNLW-JSRC也可以优于其他基于SRC的算法，即使在少量的训练样本。
Joint sparse representation classification (JSRC) is a representative spectral–spatial classifier for hyperspectral images (HSIs). However, the JSRC is inappropriate for highly heterogeneous areas due to the spatial information being extracted from a fixed-sized neighborhood block, which is often unable to conform to the naturally irregular structure of land cover. To address this problem, a superpixel-based JSRC with nonlocal weighting, i.e., superpixel-based nonlocal weighted JSRC (SNLW-JSRC), is proposed in this paper. In SNLW-JSRC, the superpixel representation of an HSI is first constructed based on an entropy rate segmentation method. This strategy forms homogeneous neighborhoods with naturally irregular structures and alleviates the inclusion of pixels from different classes in the process of spatial information extraction. Afterwards, the superpixel-based nonlocal weighting (SNLW) scheme is built to weigh the superpixel based on its structural and spectral information. In this way, the weight of one specific neighboring pixel is determined by the local structural similarity between the neighboring pixel and the central test pixel. Then, the obtained local weights are used to generate the weighted mean data for each superpixel. Finally, JSRC is used to produce the superpixel-level classification. This speeds up the sparse representation and makes the spatial content more centralized and compact. To verify the proposed SNLW-JSRC method, we conducted experiments on four benchmark hyperspectral datasets, namely Indian Pines, Pavia University, Salinas, and DFC2013. The experimental results suggest that the SNLW-JSRC can achieve better classification results than the other four SRC-based algorithms and the classical support vector machine algorithm. Moreover, the SNLW-JSRC can also outperform the other SRC-based algorithms, even with a small number of training samples.
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DOI：
10.1002/047134608x.w8276
发表时间：
2015-09
期刊：
--
影响因子：
--
作者：
Meng Yang
通讯作者：
Meng Yang
通过融合相关系数和联合稀疏表示的高光谱图像分类
影响因子：
4.8
作者：
Tu Bing;Zhang Xiaofei;Kang Xudong;Zhang Guoyun;Wang Jinping;Wu Jianhui
通讯作者：
Wu Jianhui
DOI：
10.3390/rs9090872
发表时间：
2017-08
期刊：
Remote. Sens.
影响因子：
--
作者：
Fei Tong;Hengjian Tong;Junjun Jiang;Yun Zhang
通讯作者：
Fei Tong;Hengjian Tong;Junjun Jiang;Yun Zhang
基于多尺度变换和稀疏表示的图像融合通用框架
影响因子：
18.6
作者：
Liu, Yu;Liu, Shuping;Wang, Zengfu
通讯作者：
Wang, Zengfu
影响因子：
8.2
作者：
K. Ma;Chein-I. Chang
通讯作者：
K. Ma;Chein-I. Chang