基于同类邻点预测的高光谱图像无损压缩

提出了一种基于聚类、对类波段重排、搜索同类相邻点预测的高光谱数据无损压缩方法。根据谱向特征,进行高光谱图像按像素聚类,对各个分类,根据波段间相关系数,利用最大生成树进行波段重排。对每种分类像素,搜索同类邻点,训练预测系数,并进行三维预测。残差采用Golomb-Rice编码。实验证实了算法的有效性。

Lossless Compression of Hyperspectral Images Based on a Neighbor-in-same-Cluster Prediction

Based on clustering,band reordering and predicting with adjacent pixels in the same cluster,a lossless compression method of hyperspoctral images is presented.Firstly,according to spectral structure,the spectra of a hyperspectral image are clustered by pixels.In every cluster,according to the correlation coefficient between bands,the images are reordered through maximum spanning tree. Secondly,neighbor points are searched in the same cluster,and linear prediction is performed in three dimensions.The residuals are en-tropy-coded using the Rice coding.The experimental results have shown that the algorithm is an efficient method.