最大误差可控的高光谱图像聚类压缩算法

针对原有基于奇异值分解的最大误差可控的高光谱图像压缩(EC-SVD)算法未充分利用图像光谱矢量间冗余的问题，该文将高光谱图像压缩与聚类结合，提出最大误差可控的高光谱图像聚类压缩算法。分析发现，图像的光谱矢量间相似度越高越有利于得到好的最终压缩效果。因此，算法首先使用K-均值聚类对高光谱图像像元按光谱矢量聚类，以提高同类光谱矢量间的相似度；其次，对每一类像元分别使用EC-SVD算法思想压缩以控制最大误差。论文证明了当高光谱图像的像元个数与波段数之比较大，且聚类类数不大于8时，聚类能够提高图像最终压缩比。最后，设计整体压缩实验仿真流程，并对实际高光谱图像进行数值仿真。结果表明，在相同参数条件下，该文算法比EC-SVD算法得到的压缩比和信噪比均有提高，最大压缩比提高了10% 左右。该文算法能够有效提高EC-SVD算法的图像压缩效果。

Hyperspectral Image Compression Algorithm with Maximum Error Controlled Based on Clustering

Aiming at the problem that the maximum Error Controllable compression based on SVD (EC-SVD) algorithm can not make full use of spectral vectors’ redundancy in hyperspectral image, a hyperspectral image compression algorithm with maximum error controlled based on clustering is presented in this paper, by combining hyperspectral image compression with clustering. It is found that a higher compression ratio can be achieved as spectral vectors’ similarity increases. Using the K-means clustering algorithm, the pixels of hyperspectral image are clustered by spectral vectors to improve the similarity of spectral vectors in the same class. Then, the pixels in each class are compressed using the idea of EC-SVD algorithm. And it is shown that the compression ratio increases if the cluster number is no more than 8 and the number of pixels is larger than that of bands in the clustered hyperspectral image. Finally, a total simulation procedure of the improved compression algorithm is designed and some hyperspectral images are tested. The results of the tests show that compression ratios and signal to noise ratios are higher than those of EC-SVD algorithm under the same parameters; the maximum compression ratio rises around 10 percent. The presented improved algorithm can raise the compression efficiencies of hyperspectral images.