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基于K-均值聚类和传统递归最小二乘法的高光谱图像无损压缩

高放 孙长建 邵庆龙 郭树旭

高放, 孙长建, 邵庆龙, 郭树旭. 基于K-均值聚类和传统递归最小二乘法的高光谱图像无损压缩[J]. 电子与信息学报, 2016, 38(11): 2709-2714. doi: 10.11999/JEIT151439
引用本文: 高放, 孙长建, 邵庆龙, 郭树旭. 基于K-均值聚类和传统递归最小二乘法的高光谱图像无损压缩[J]. 电子与信息学报, 2016, 38(11): 2709-2714. doi: 10.11999/JEIT151439
GAO Fang, SUN Changjian, SHAO Qinglong, GUO Shuxu. Lossless Compression of Hyperspectral Images Using K-means Clustering and Conventional Recursive Least-squares Predictor[J]. Journal of Electronics and Information Technology, 2016, 38(11): 2709-2714. doi: 10.11999/JEIT151439
Citation: GAO Fang, SUN Changjian, SHAO Qinglong, GUO Shuxu. Lossless Compression of Hyperspectral Images Using K-means Clustering and Conventional Recursive Least-squares Predictor[J]. Journal of Electronics and Information Technology, 2016, 38(11): 2709-2714. doi: 10.11999/JEIT151439

基于K-均值聚类和传统递归最小二乘法的高光谱图像无损压缩

doi: 10.11999/JEIT151439
基金项目: 

国家自然科学基金(41101419)

Lossless Compression of Hyperspectral Images Using K-means Clustering and Conventional Recursive Least-squares Predictor

Funds: 

The National Natural Science Foundation of China (41101419)

  • 摘要: 针对基于预测的高光谱图像无损压缩算法压缩比低的问题,该文将聚类算法与高光谱图像预测压缩算法相结合,提出一种基于K-均值聚类和传统递归最小二乘法的高光谱图像无损压缩算法。首先,对高光谱图像按光谱矢量进行K-均值聚类以提升同类光谱矢量间的相似度。然后,对每一聚类群分别使用传统递归最小二乘法进行预测,消除高光谱图像的空间冗余和谱间冗余。最后,对预测误差图像进行算术编码,完成高光谱图像压缩过程。对AVIRIS 2006高光谱数据进行仿真实验,所提算法对16位校正图像、16位未校正图像和12位未校正图像分别取得了4.63倍,2.82倍和4.77倍的压缩比,优于同类型已报道的各种算法。
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出版历程
  • 收稿日期:  2015-12-22
  • 修回日期:  2016-04-08
  • 刊出日期:  2016-11-19

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