基于分类的高光谱图像压缩算法

高光谱图像庞大的数据量给存储与传输带来巨大挑战,必须采用有效的压缩算法对其进行压缩。提出了一种基于分类的高光谱图像有损压缩算法。首先利用C均值算法对高光谱图像进行无监督光谱分类。根据分类图,针对每一类数据分别采用自适应KLT(Karhunen-Love transform)进行谱间去相关;然后对每个主成分分别进行二维小波变换。为了获得最佳的率失真性能,采用EBCOT(Embedded Block Coding with Optimized Truncation)算法对所有的主成分进行联合率失真编码。实验结果表明,所提出算法的有损压缩性能优于其它经典的压缩算法。

Class-based compression algorithm for hyperspectral images

The huge amount of hyperspectral images creates challenges for data storage and transmission, thus it is necessary to employ efficient algorithm for hyperspectral images compression. An efficient lossy compression algorithm based on spectral classification was presented in this paper. The C-means algorithm was performed on the hyperspectral images to realize the unsupervised classification. According to the classification map, an adaptive Karhunen-Love transform was performed on each class vector with the same spatial location in the spectral orientation to remove the spectral correlation, and then two dimensional wavelet transform was performed on each principle component. In order to achieve the best rate-distortion performance, the embedded block coding with optimized truncation coding was performed on all the principle components to produce the final bit-stream. Experimental results show that the proposed algorithm outperforms other state-of-the-art algorithms.