基于分类的高光谱图像压缩算法（英文）  

Class-based compression algorithm for hyperspectral images

作　　者：杨新锋[1] 胡旭诺[2] 粘永健[3] 

机构地区：[1]南阳理工学院计算机与信息工程学院,河南南阳473000 [2]南阳医学高等专科学校卫生管理系,河南南阳473000 [3]第三军医大学生物医学工程学院,重庆400038

出　　处：《红外与激光工程》2016年第2期263-266,共4页Infrared and Laser Engineering

基　　金：国家自然科学基金(41201363);河南省重点科技攻关项目(122102210563;132102210215)

摘　　要：3.第三军医大学生物医学工程学院,重庆400038)摘要: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-Loève 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。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-Lo^ve 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.

关 键 词：有损压缩 高光谱图像 光谱分类 光谱去相关 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]