中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Lossless compression of hyperspectral imagery using a fast adaptive-length-prediction RLS filter

文献类型:期刊论文

作者Song, Jinwei; Zhou, Li; Deng, Chao; An, Junshe
刊名REMOTE SENSING LETTERS
出版日期2019
卷号10期号:4页码:401-410
ISSN号2150-704X
DOI10.1080/2150704X.2018.1562257
英文摘要

Recursive Least Square (RLS) filter has been applied to real-time lossless compression of hyperspectral imagery and been proved a high performance onboard algorithm. Recent research has revealed that the RLS filter with Adaptive-Length-Prediction (ALP) can significantly improve the compression performance. However, the prediction procedure with numerous bands slows down the run-time and is nearly impossible to be applied onboard. In this letter, we proposed a fast RLS algorithm which can accelerate the ALP stage by exploiting the feature of the projection matrix of the RLS algorithm. The experiment results illustrated that with the same compression ratio, the proposed algorithm is 100 times faster than the traditional RLS algorithm with ALP.

源URL[http://ir.nssc.ac.cn/handle/122/6674]  
专题国家空间科学中心_空间技术部
推荐引用方式
GB/T 7714
Song, Jinwei,Zhou, Li,Deng, Chao,et al. Lossless compression of hyperspectral imagery using a fast adaptive-length-prediction RLS filter[J]. REMOTE SENSING LETTERS,2019,10(4):401-410.
APA Song, Jinwei,Zhou, Li,Deng, Chao,&An, Junshe.(2019).Lossless compression of hyperspectral imagery using a fast adaptive-length-prediction RLS filter.REMOTE SENSING LETTERS,10(4),401-410.
MLA Song, Jinwei,et al."Lossless compression of hyperspectral imagery using a fast adaptive-length-prediction RLS filter".REMOTE SENSING LETTERS 10.4(2019):401-410.

入库方式: OAI收割

来源:国家空间科学中心

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