Parallelizing Band Selection for Hyperspectral Imagery with Many-Threads

被引：0
|
作者：
Gan, Xinbiao [1 ,2 ,3 ]
Liu, Jie [1 ]
Gan, Xinbiao [1 ,2 ,3 ]
机构：
[1] Natl Univ Def Technol, Sch Comp, Changsha, Hunan, Peoples R China
[2] Chinese Acad Sci, Sate Key Lab Space Weather, Beijing, Peoples R China
[3] Chinese Acad Sci, Key Lab Spectral Imaging Technol, Xian, Shaanxi, Peoples R China
来源：
2017 2ND INTERNATIONAL CONFERENCE ON IMAGE, VISION AND COMPUTING (ICIVC 2017) | 2017年
基金：
中国国家自然科学基金;
关键词：
band selection; K-L divergence; China accelerator; many-threads;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
It is time-consuming and expensive for band selection of hyperspectral imagery. In practice, band selection for hyperspectral imagery is a computing-intensive application, in which bands with less information are removed and the maximal information band should be preserved by quantifying information amount based on K-L divergence. Fortunately, it is suitable to parallelize band selection for hyperspectral imagery using accelerator with many-threads. Experimental results validate that band selection with China Accelerator would be much better than CPU and 1.25 X speedups than that of matched GPU.
引用
收藏
页码：505 / 509
页数：5
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