基于经验模式分解的数字高程模型数据伪装方法  

Information Disguising for Digital Elevation Model Data via Empirical Mode Decomposition

作　　者：刘水强[1] 陈继业[2] 朱鸿鹏[1] 

机构地区：[1]邵阳学院网络信息中心,邵阳市邵水西路422000 [2]邵阳学院理学与信息科学系,邵阳市邵水西路422000

出　　处：《武汉大学学报（信息科学版）》2008年第6期652-655,共4页Geomatics and Information Science of Wuhan University

基　　金：湖南省科技计划资助项目(06CK3047);湖南省教育厅重点资助项目(06A066);湖南省教育厅科研项目(06C753)

摘　　要：提出了一种基于经验模态分解(EMD)的数字高程模型数据伪装技术。首先利用SHA-256单向Hash函数产生由种子控制的伪随机序列,扩充序列后再用经验模态分解生成用于伪装的DEM数据,伪装后的DEM数据具有较高的视觉欺骗性。针对DEM数据提出了直方图的概念,通过修改直方图,在伪装的DEM数据中可逆地嵌入水印。本文方法可在提取水印后完全恢复伪装DEM数据,以及使用种子可完全还原秘密DEM数据,算法安全性高。A novel information disguising method based on empirical mode decomposition is proposed. The pseudorandom sequence controlled by seeds of the SHA-256 one-way hash function is generated; and digital elevation model data for disguising is achieved by decomposing the expanded pseudorandom sequence via empirical mode decomposition（EMD）. The high vision fraudulence is obtained for disguised DEM data. Furthermore, the concepts of the histogram for DEM data is also proposed; and the watermarking was reversibly embed- ded in the disguised DEM data by modifying its histogram. The disguised DEM data can be completely reconstructed without any distortion from the marked data after the watermark has been extracted. The secret DEM data can be recovered via the seed. The proposed algorithm owns high security.

关 键 词：信息伪装 数字高程模型 经验模态分解 直方图 可逆数字水印 

分 类 号：P237.3[天文地球—摄影测量与遥感] P231.5[天文地球—测绘科学与技术]