摘要
基于矢量量化的高光谱图像有损压缩算法可以获得较高的压缩比,但是其时间复杂度高,失真较大.为此,提出了模拟退火优化模糊C均值聚类(FCM)的高光谱图像有损压缩算法.先对高光谱图像进行自适应波段合并算法降维,利用肘部现象确定量化级数,结合模拟退火的全局寻优能力和模糊聚类的快速收敛能力,找到最优解后恢复维度,最后去模糊优化编码方案.通过这种方法,在提高高光谱图像压缩运算效率和减小解压后失真方面都有了较大的优化,是基于矢量量化的高光谱图像压缩的可行方法.
Lossy compression of hyperspectral image based on vector quantization algorithms can achieve a high compression ratio,but it is of time complexity and great distortion. This article proposed a new fuzzy C-means clustering（ FCM） algorithm based on simulated annealing. Firstly,the dimensions were reduced by using the algorithm of adaptive band combination dimensional reduction（ ABC）,then the number of clusters with the elbow was determined. FCM was combined with simulated annealing,and found optimal result quickly,then recovered dimensions. We got optimization coding by deblurring U.Through this approach,the efficiency has been improved and the distortions have been reduced greatly.
出处
《北京邮电大学学报》
EI
CAS
CSCD
北大核心
2015年第5期58-61,70,共5页
Journal of Beijing University of Posts and Telecommunications
基金
国家高技术研究发展计划(863计划)项目(1212011120221)
关键词
模拟退火
模糊聚类
降维
肘部现象
矢量量化
simulated annealing
fuzzy C-means clustering
dimension reduction
the elbow
vector quantization
