文档介绍：基于3DLMS预测的高光谱图像无损压缩算法*
陈永红1,2,史泽林1,李德强1
(1 中国科学院沈阳自动化研究所沈阳 110016;2 中国科学院研究生院北京 100039)
摘要:针对高光谱图像无损压缩比较低的问题,将三维LMS算法(3DLMS)应用于高光谱图像压缩领域,利用3DLMS算法构造了一种新的高光谱图像自适应预测模型,通过去局部因果集均值方法实现了模型优化。对不同场景AVIRIS图像的实验表明,基于3DLMS预测的高光谱图像无损压缩算法同时降低了高光谱图像的空间和谱间冗余,提高了高光谱图像的无损压缩比,同时该方法也验证了3DLMS算法在高光谱图像压缩中的可行性。
关键词:3DLMS;高光谱图像;无损压缩;自适应预测
中图分类号: 文献标识码:A 国家标准学科分类代码:
pression algorithm of hyperspectral image
based on 3DLMS prediction
Chen Yonghong1,2, Shi Zelin1, Li Deqiang1
(1 Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
2 Graduate School of the Chinese Academy of Sciences, Beijing 100039, China)
Abstract:Aiming at the low pression ratio of hyperspectral image, three-dimensional LMS (3DLMS) algorithm is applied to the field of hyperspectral pression. A novel adaptive prediction model based on 3DLMS algorithm for pression of hyperspectral image is proposed and is optimized by the local casual set mean subtraction method. Experimental results of AVIRIS images show that the proposed algorithm can remove the spatial and spectral redundancy of hyperspectral image concurrently and achieve higher pression ratio than other state-of-the-pression algorithms. The feasibility of 3DLMS algorithm in hyperspectral pression is also validated in this paper.
Key words:3DLMS; hypersepctral image; pression; adaptive prediction
1 引言
高光谱图像的海量数据给容量受限的星上存储