Quantitative Analysis on Lossy Compression in Remote Sensing Image Classification

被引：2
|
作者：
Xia, Yatong [1 ]
Li, Zimeng [1 ]
Chen, Zhenzhong [1 ]
Yang, Daiqin [1 ]
机构：
[1] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Hubei, Peoples R China
来源：
VISUAL INFORMATION PROCESSING AND COMMUNICATION VI | 2015年 / 9410卷
关键词：
Remote sensing image compression; Classification accuracy; LS-SVM; ALGORITHMS; SVM;
D O I：
10.1117/12.2083205
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we propose to use a quantitative approach based on LS-SVM to perform estimation of the impact of lossy compression on remote sensing image compression. Kernel function selection and the model parameters computation are studied for remote sensing image classification when LS-SVM analysis model is establish. The experiments show that our LS-SVM model achieves a good performance in remote sensing image compression analysis. Classification accuracy variation according to compression ratio scales are summarized based on our experiments.
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收藏
页数：8
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