计算机工程与应用2016，Vol.52Issue(11)：190-195,6.DOI:10.3778/j.issn.1002-8331.1407-0301
基于稀疏度自适应化的DEM压缩采样与重构方法
DEM compressive sampling and reconstruction based on sparsity adap-
tive method
陈宇峰 1常慧杰 1聂睿2
作者信息
- 1. 北京理工大学 计算机学院,北京 100081
- 2. 中国飞行试验研究院,西安 710089
- 折叠
摘要
Abstract
The ever increasing volume of terrain data require efficient strategies in storage and transmission. In this paper, a new DEM compressive sampling and reconstruction based on sparsity adaptive method is proposed. The detailed step and flow chart of the method are shown. In the method, the curvelet transform can be utilized to make DEM sparse firstly, and then the random Gaussian matrix after approximate orthogonal-matrix and Right-matrix(QR)decomposition can be employed to complete the low-dimension measurement and finish compress the DEM. Furthermore, the modified CoSaMP algorithm, inverse curvelet transform and so on can be used to achieve the final reconstruction. In this paper, on the condi-tion of the same reconstruction precision, the modified CoSaMP algorithm conducts blind recovery without priori infor-mation of sparsity and the convergence speed is enhanced compared with the original CoSaMP algorithm. Experimental results indicate that compared with the JPEG2000 the proposed method obtains high compression ratio and high recon-struction accuracy.关键词
改进的CoSaMP算法/数字高程模型(DEM)/压缩感知/小波变换Key words
modified CoSaMP algorithm/Digital Elevation Model(DEM)/compressed sensing/wavelet transform分类
信息技术与安全科学引用本文复制引用
陈宇峰,常慧杰,聂睿..基于稀疏度自适应化的DEM压缩采样与重构方法[J].计算机工程与应用,2016,52(11):190-195,6.
