基于Shearlet和稀疏表示的遥感图像融合  

Remote Sensing Image Fusion Based on Shearlet and Sparse Representation

作　　者：赵学军[1] 刘静[1] 

机构地区：[1]中国矿业大学(北京)机电与信息工程学院,北京100083

出　　处：《科学技术与工程》2017年第4期255-259,共5页Science Technology and Engineering

基　　金：国家高技术研究发展计划(2012AA12A308);国家地质调查项目(1212011120221);国土资源部公益性行业科研专项(201211003)资助

摘　　要：针对NSCT变换算法具有较高复杂度、计算时间长、不符合实时性要求的问题,提出将基于Shearlet变换和稀疏表示的算法引入到遥感图像融合中。首先,对待融合图像进行Shearlet变换,分解后得到的低频子带系数采用区域能量取大的融合规则;分解后的高频子带系数采用PCNN的融合规则,最后对重构系数进行Shearlet逆变换。实验结果表明,与NSCT变换及经典算法相比,新方法不仅有效改善了图像融合质量,同时提升了算法的运行速度,实时性良好。According to NSCT with high complexity, long computing time and non-conformance to real-time, in-troducing algorithm based on Shearlet transform and sparse representation was put forward into remote sensing image fusion. Through Shearlet transform original images are divided into low and high frequency subbands. The rule of re-gional energy was usied to fuse the former and the rule of PCNN to fuse the latter; Finally, the new multi spectral image can be obtained by the Shearlet inverse transform. Experimental results show that compared with the NSCT and classical algorithm, the new method not only effectively improves the quality of image fusion, but also increases the speed of the algorithm and has good real-time performance.

关 键 词：图像融合 Shearlet 稀疏表示 区域能量 PCNN 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]