作者：杨蕴睿1, 郑东文2
作者单位：1. 郑州工商学院工学院，河南 郑州 451400;
2. 河南科技大学信息工程学院，河南 洛阳 471023
关键词：高光谱遥感图像;高光谱图像分类;加权滤波;空间光谱信息
摘要：
针对现有基于协同表示的分类算法对于高光谱遥感图像的空间光谱信息利用不充分而造成较低分类精度的问题，该文提出一种空谱协同编码方法用于高光谱图像分类。算法首先利用空间光谱信息对图像进行加权滤波。随后，对于协同编码模型，将空间光谱信息转化为空间光谱权重以对模型进行正则约束。在Indian Pines和University of Pavia真实数据集上的实验结果表明提出的算法能分别获得98.82%和99.09%的总体精度。实验证明了所提出的算法对高光谱遥感图像进行分类的有效性。
Spatial-spectral collaborative coding for hyperspectral image classification
YANG Yunrui1, ZHENG Dongwen2
1. School of Computer Engineering, Zhengzhou Technology and Business University, Zhengzhou 451400, China;
2. College of Information Engineering, Henan University of Science and Technology, Louyang 471023, China
Abstract: Aiming at the problem that the existing classification algorithms based on collaborative representation do not make full use of the spatial spectral information of hyperspectral remote sensing images, resulting in low classification accuracy, a spatial spectral collaborative coding method is proposed for hyperspectral image classification. Firstly, the image is smoothed by spatial spectral weighted filtering method. Then, for the collaborative coding model, the spatial spectral information is transformed into spatial spectral weight to regularize the model. The experimental results on Indian Pines and University of Pavia real data sets show that the proposed algorithm can obtain 98.82% and 99.09% overall accuracy, respectively. Experiments show that the proposed algorithm is effective in classifying hyperspectral remote sensing images.
Keywords: hyperspectral remote sensing image;hyperspectral image classification;weighted filtering;spatial-spectral information
2022, 48(12):162-171 收稿日期: 2021-08-12;收到修改稿日期: 2021-11-08
基金项目: 国家自然科学基金（61801170）
作者简介: 杨蕴睿(1982-),女,河南郑州市人,讲师,硕士,研究方向为计算机技术
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