摘要
针对压缩感知(compressive sensing,CS)在遥感成像应用中存在的若干瓶颈,提出一种基于多尺度透镜组的分形压缩感知成像方法。一方面,通过多尺度透镜组避免了基于稀疏表示的CS成像方式在大视场角观测条件下出现海量运算开销的问题;另一方面,运用分形维度代替l1范数最小化作为求解CS成像问题中的目标函数,实现了中、高分辨率遥感成像在图像细节水平上的质量提升。试验表明,多尺度分形压缩感知成像方法与传统CS成像相比,不仅能达到遥感成像的时效性要求,而且其细节层次上的成像质量也大幅提高。
A novel fractal compressive sensing （CS） imaging method is proposed in this paper to solve some bottlenecks of CS application in remote sensing imaging. On the one hand, multi-scale lens would be used to reduce the massive computation cost, which is caused by the traditional CS imaging based on sparse representation under large field of view. On the other hand, l1-norm minimization, as the object function ol traditional CS imaging, is replaced by fractal dimension for improving the quality of middle and high resolution remote sensing imaging in image details. Experimental results show that, multi-scale fractal CS imaging method can not only meet the timeliness requirement of remote sensing imaging but also improve the imaging quality in details level.
出处
《测绘学报》
EI
CSCD
北大核心
2013年第6期846-852,共7页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(61273251)
关键词
压缩感知
遥感成像
多尺度透镜组
L1
范数最小化
分形维度
compressed sensing
remote sensing imaging
multi-scale lens
l1 -norm minimization
fractal dimension
