Classification method of CO2 hyperspectral remote sensing data based on neural network

被引：11
|
作者：
Zhang, Le [1 ,2 ]
Wang, Jinsong [2 ]
An, Zhiyong [2 ]
机构：
[1] Shenyang Ligong Univ, Equipment Engn Dept, Shenyang 100168, Peoples R China
[2] Changchun Univ Sci & Technol, Optoelect Engn Dept, Changchun 130022, Jilin, Peoples R China
来源：
COMPUTER COMMUNICATIONS | 2020年 / 156卷
关键词：
Hyperspectral; Remote sensing image; Neural network; Carbon dioxide; Support vector machine;
D O I：
10.1016/j.comcom.2020.03.045
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Based on the dimension reduction of hyperspectral remote sensing image, a new neural network method is used to classify the hyperspectral remote sensing image of carbon dioxide in detail. Firstly, the Kernel Principal Component Analysis (KPCA) and Genetic Algorithms (GA) are used to reduce the dimension of hyperspectral remote sensing images; secondly, the traditional remote sensing image classification methods (ISODATA, SVM), traditional neural networks (BP), and new neural networks are used to classify the hyperspectral remote sensing images. Finally, noise assessment method based on local mean and local standard deviation (LMLSD) of spectral image is used to evaluate the classification accuracy. In addition, hyperspectral remote sensing images are dimensionality reduction. Secondly, the comparison between the traditional remote sensing image classification method and the new neural network method is analyzed. Finally, a new neural network method is applied to classify hyperspectral remote sensing images.
引用
收藏
页码：124 / 130
页数：7
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