• 遥感科学与应用技术 •

### 基于加权指数函数模型的高光谱图像分类方法

1. 1.辽宁工程技术大学 测绘与地理科学学院 遥感科学与应用研究所,阜新 123000
2.自然资源部国土卫星遥感应用中心,北京 100048
• 收稿日期:2019-07-18 修回日期:2019-11-27 出版日期:2020-08-25 发布日期:2020-10-25
• 通讯作者: 李奕燃 E-mail:liyu@lntu.edu.cn;939902034@qq.com
• 作者简介:李 玉(1963— ),男,辽宁康平人,教授,博士生导师,主要从事遥感数据处理理论与应用基础研究,包括空间统计学、随机几何、模糊数学在遥感数据建模与分析方面的应用。E-mail:liyu@lntu.edu.cn
• 基金资助:
国家自然科学基金项目(41301479);辽宁省高校创新人才支持计划项目(LR2016061)

### A Hyperspectral Image Classification Algorithm based on the Weighted Exponential Function Model

LI Yu1(), LI Yiran1,*(), WANG Guanghui2, SHI Xue1

1. 1. The Institute for Remote Sensing, School of Geomatics, Liaoning Technology University, Fuxin 123000, China
2. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
• Received:2019-07-18 Revised:2019-11-27 Online:2020-08-25 Published:2020-10-25
• Contact: LI Yiran E-mail:liyu@lntu.edu.cn;939902034@qq.com
• Supported by:
National Natural Science Foundation of China(41301479);Innovative Talents Support Program for Colleges and Universities in Liaoning Province(LR2016061)

Abstract:

In recent years, the hyperspectral remote sensing technology has developed rapidly. Hyperspectral images obtained by hyperspectral sensors contain abundant spectral information of ground objects, such that they are good for fine spectral recognition. In hyperspectral image processing, its accurate classification is a solid foundation for the subsequent interpretation tasks. However, the numerous bands in hyperspectral imagery not only provide sufficient characteristics information for classification, but also bring the problem of how to use these characteristics effectively. In this paper, to make full use of the spectral information of hyperspectral images so to achieve accurate classification, a hyperspectral image classification method based on the Weighted Exponential Function (WEF) was proposed that considers the multi-peak characteristics of the spectral response curve of pixels. Firstly, the WEF model was used to build an ideal model of the spectral response curve of pixels, composed of several exponential functions with different weights. Because there are many parameters in the model (including weight, peak position and peak width), it is difficult to solve them. Therefore, the WEF model with fixed peak positions and number of exponential functions was used to model the spectral response curve of all pixels. Then, the parameters of the WEF model were determined according to the least square principle to fit the spectral response curve. Finally, the parameter set was used to replace the spectral measure vector, and the WEF model parameter vector of the pixel was used as its feature. Fuzzy C-means (FCM) algorithm was used for image classification. To validate the feasibility and effectiveness of the proposed method, the classification experiments of Salinas and PaviaU hyperspectral images were conducted by using respectively the proposed method, Principal Component Analysis (PCA) based classification method, Minimum Noise Fraction (MNF) based classification method, and FCM method with the spectral measure vector as the classification feature. The user accuracy, product accuracy, overall accuracy and Kappa coefficient of the results from these classification methods were calculated, and the experimental results were evaluated qualitatively and quantitatively. Compared with other methods, the classification accuracy of the proposed classification method for the Salinas image increased from 51% to 60%, and for the PaviaU image from 43% to 51%. In addition, the proposed classification method reduced the amount of hyperspectral image data while preserving the rich spectral information of hyperspectral images.