摘要
高光谱数据的有效压缩成为遥感技术发展中需要迫切解决的问题。提出了一种基于分类非线性预测的高光谱图像无损压缩算法。针对不同频谱波段间相关性不同的特点,根据相邻波段相关性大小进行波段分组。为提高谱间预测性能,对各组波段进行最优排序。采用自适应波段选择算法对高光谱图像进行降维,并利用k-means算法对降维后波段的谱向矢量进行分类。在参考波段和预测波段中选取具有相同空间位置的上下文结构,在分类结果的基础上,对当前波段进行谱间非线性预测。参考波段采用JPEG-LS标准进行压缩,预测残差进行Golomb-Rice编码。对AVIRIS型高光谱图像的实验结果表明,该算法可显著降低压缩后的平均比特率。
The request for efficient compression of hyperspectral imagery becomes pressing. Cluster-based non-linear predictive lossless compression for hyperspectral imagery is presented. Due to the spectral correlation differs in different bands, spectral band grouping algorithm is introduced to divide hyperspectral images into groups according to the correlation between each adjacent bands. The important bands containing large information can be determined by using the adaptive band selection, on which k-means clustering is carried out according to the spectral vectors. Prediction contexts are defined based on the neighboring causal pixels in the current band and the corresponding co-located causal pixels in the reference band. The current pixel is predicted by using inter-band non-linear prediction with the prediction contexts. The reference bands are compressed by JPEG-LS standard while the final predictive errors are coded by Golomb-Rice. Experimental results show that the proposed algorithm can ~ive better lossless coding performance.
出处
《信号处理》
CSCD
北大核心
2009年第8期1223-1227,共5页
Journal of Signal Processing
基金
国家自然科学基金资助项目(No.60572135)
武器装备预研基金资助项目(No.9140A22020607KG0181)
关键词
高光谱图像
无损压缩
波段排序
非线性预测
hyperspectral imagery
lossless compression
band reordering
nonlinear prediction
