基于聚类的高光谱图像无损压缩

高光谱海量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。该文提出了一种基于聚类的高光谱图像无损压缩算法。针对高光谱图像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。由于高光谱图像波段数量较多，采用自适应波段选择算法对高光谱图像进行降维，以获取信息量较大的部分波段，利用k均值算法对降维后的波段谱矢量进行聚类。采用多波段预测的方案对各组中的波段进行预测，对于各个分类中的每个像素，分别选取与其空间相邻的已编码的部分同类点进行训练，从而获得当前像素的谱间最优预测系数。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。

Lossless Coding for Hyperspectral Images Based on Spectral Cluster

The request for efficient compression of hyperspectral images becomes pressing. A cluster-based lossless compression algorithm for hyperspectral images is presented. Because 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 which contain large useful information can be determined by using the adaptive band selection algorithm, on which k-means clustering is carried out according to the spectral vectors. The current band is predicted by using several preceding bands. For each pixel which belongs to a certain cluster, some causal neighboring pixels which have been coded are trained to get the optimal predictive coefficients. 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 methods produce competitive results when compared with other state-of-the-art algorithms.