基于分块KLT的多光谱遥感图像低复杂度有损压缩  

Low-Complexity Lossy Compression for Multispectral Remote Sensing Images Based on Block KLT

作　　者：王平 陈欣 粘永健[2] 许可[2] 

机构地区：[1]重庆工程学院,软件与计算机学院,重庆400056 [2]国防科技大学,电子科学与工程学院,湖南长沙410073

出　　处：《红外技术》2018年第2期151-157,共7页Infrared Technology

基　　金：国家自然科学基金项目(41201363),重庆市基础科学与前沿技术项目(cstc2016jcyjA0539).

摘　　要：多光谱图像的有效压缩已经成为遥感领域亟待解决的难题。针对星载多光谱成像仪获取的多光谱图像,提出了一种基于分块KLT(Karhunen-Loève transform,卡胡南-洛维变换)的低复杂度有损压缩算法。该算法首先对每个波段分别进行空间二维小波变换,以去除多光谱图像的空间相关性;然后将每个波段分成互不重叠且大小相等的图像块,每次仅对相邻两个波段的对应图像块进行谱间KLT变换,以去除谱间相关性;最后对变换后的所有波段进行联合EBCOT(Embedded Block Coding with Optimized Truncation,最优截断的嵌入式块编码)压缩。实验结果表明,该算法的压缩性能优于基于整体KLT的多光谱图像压缩算法,并且具有较低的编码复杂度。Efficient compression of multispectral images has been a persistent problem in the field of remote sensing. As for the multispectral images captured by satellite, a block-based KLT lossy compression with low complexity is proposed. First, a two-dimensional discrete wavelet transform is performed on each band of the multispectral images to remove spatial correlation. Subsequently, each band is partitioned into non-overlapping blocks of the same size; blocks that are co-located on adjacent two bands are subjected to an adaptive Karhunen-Loève transform to remove their spectral correlation. Finally, the optimal truncation technique of post-compression and rate-distortion optimization is employed for rate allocation to multiple bands, followed by embedded block coding with optimized truncation to generate the final bit-stream. Experimental results show that the proposed algorithm not only outperforms the algorithm based on global KLT, but also has low encoder complexity.

关 键 词：多光谱图像 低复杂度压缩 光谱去相关 分块KLT 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]