一种用于篡改定位及近似恢复的水印算法

针对遥感影像易被非法篡改的问题,提出了一种用于篡改定位及近似恢复的遥感影像半脆弱水印方案。首先将二值伪随机矩阵作为认证水印,通过量化Contourlet变换各方向子带中绝对值最大的系数将其嵌入,然后采用影像4×4分块后的灰度平均值作为恢复水印,将其嵌入到最低有效位。水印检测不需要原始遥感影像参与,实现了盲提取。实验结果表明,算法对于噪声、JPEG压缩等合理性失真具有一定的鲁棒性,而对于恶意篡改等攻击具有脆弱性,能够实现篡改区域的精确定位和近似恢复,使恢复后的遥感影像满足基本的视觉要求,可有效保护遥感影像的安全。     

视觉显著性与知觉组织相结合的高分影像居民地提取方法

受人类视觉认知机制的启发,提出了一种利用视觉显著性与知觉组织相结合的高分辨率遥感影像居民地提取方法。首先利用认知物理学中的数据场构建居民地的视觉显著性模型,并通过自适应阈值法实现候选居民地的自动提取,然后利用多尺度小波变换的高频特征实现居民地的知觉组织,最后通过集合交运算提取同时满足这两种视觉机制的居民地。通过ZY-3和Quickbird两种高分传感器的影像数据集进行居民地提取试验,验证了该方法的有效性。     

Mapping cropping intensity trends in China during 1982–2013

Long range continuous monitoring information of cropping intensity is useful for sustainable agricultural management but still limited. This study filled this information gap through delivering spatiotemporal continuous datasets of cropping intensity in China during the past 30 years. Cropping intensity data were derived by a wavelet features-based method based on the long-term weekly global EVI2 (Enhance Vegetation Index with two bands) at 0.05° spatial resolution (5 km) from 1982 to 1999 and 8-day composite 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products from 2001 to 2013. The remote-sensing estimated images in 2013 agreed well with field survey data (overall accuracy = 91.63%) and the national agricultural census data (r² = 0.89). Results revealed that the cropping intensity remarkably increased during 1982–1999 but slightly declined during 2001–2013. The overall cropping intensity increased from 1.34 in the 1980s to 1.41 in the 1990s, and then dropped to an average of 1.36 after 2000. From 1982 to 1999, approximately 93,225 km² single-cropped areas changed to double-cropping, primarily those located in the North China plain. However, 39,883 km² double-cropped areas were turned back into single-cropping areas from 2001 to 2013, principally located in the North China plain, the Middle-lower Yangtze River plain, and the hill regions of the southern Yangtze River. This reverse trend of cropping intensity was due to combined effects from the corresponding reverse variations in agricultural population, increasing agricultural mechanical power, positive agricultural policy. The agricultural duty free policy has only immediate effects on stabilizing cropping intensity in croplands with more favorable biophysical conditions.     

分数阶傅里叶变换联合支持向量机的建筑物变形预测

提出一种分数阶傅里叶变换(fractional Fourier transformation, FrFT)与支持向量机(support vector machine, SVM)相结合的建筑物变形组合预测模型。首先利用FrFT对变形时间序列进行多尺度分析,将复杂时间序列分解为一系列结构较为简单的子序列;然后利用SVM对每个子序列分别建立预测模型,通过将各个子序列的预测结果进行综合叠加,得到最终预测结果;同时考虑到SVM模型参数选择的难题,提出一种改进果蝇优化算法(improved fruit fly optimization algorithm, IFOA)对其进行全局寻优,提升预测性能。以西南地区某混凝土坝变形实测数据为例开展验证实验,结果表明,本文组合预测模型能够充分挖掘数据中隐含的趋势性和规律性信息,获得较高的预测精度。     

中生代东亚大陆边缘构造演化

摘　要　根据东亚陆缘增生带生物古地理、放射虫时代研究的进展并结合同位素年代及东亚 地区火山活动、构造演化探讨了中生代东亚大陆与古太平洋板块之间的运动学关系及俯冲带 后退特征。中、晚三叠世那丹哈达岭、美浓等地体还位于北纬１２°以内及赤道附近,晚侏罗世 到达中高纬度。东亚活动大陆边缘开始于中侏罗世末,在此之前属转换大陆边缘。洋壳板块 向大陆下俯冲之后,由于地体拼贴引起俯冲带快速、长距离后退。     

多分辨率地震信号反褶积

基于二进小波变换提出了一种新的反褶积方法─-多分辨率地震信号反褶积．在地震信号二进小波变换域中的各尺度上分别进行其分辨率随小波尺度变化的反褶积,利用不同分辨率反褶积结果之间的相关性,以及测量噪声随尺度的衰减特性,从低分辨率反褶积结果逼近高分辨率反褶积结果．理论分析和实验表明,该方法有较高的精度,并且在较低信噪比情况下有好的效果．     

多尺度τ-p谱及其应用

把小波变换与τ-ｐ变换有机地结合起来,本文提出了多尺度τ-ｐ谱的概念,给出了基于多尺度τ-ｐ谱作滤波处理和波场分离的方法．与通常的τ-ｐ变换相比,该方法将x－t域的地震记录变换到（τ,p,a）或（τ,p,f）空间（即多尺度τ-ｐ谱）．多尺度τ-ｐ谱比通常τ-ｐ谱增加了一维,因此用于波场分离或去噪,前者优于后者．若恰当地选择小波函数,该方法抗噪能力强,计算精度高．文中给出了模型及实际地震资料处理实例;证明了此方法的有效性．     

小波变换多尺度地震波形反演

完全非线性地震波形反演问题是石油地球物理勘探领域中一个非常重要而又难度很大的问题。本文提出了多尺度地震波形反演的小波变换方法,对于一维非线性地震波形反演问题,将该方法和已有的简单迭代法及多重网格法作了比较,数值实验结果表明,本方法效果较好。     

用连续小波变换分析探地雷达回波信号

脉冲探地雷达回波信号是典型的非平稳、非线性信号.本文通过连续小波变换,对地下圆柱目标实测结果进行分析,得到了目标冲击响应的典型波形特征.分析结果证明,这种方法可以有效地用于目标检测,时延估计等脉冲探地雷达信号处理中的关键问题     

Conditioning Geostatistical Operations to Nonlinear Volume Averages

The all-important process of data integration calls for algorithms that can handle secondary data often defined as nonlinear averages of the primary (hard) data over specific areas or volumes. It is suggested to approximate these nonlinear averages by linear averages of a nonlinear transform of the primary variable. Kriging of such nonlinear transforms, followed by the inverse transform, allows exact reproduction of all original data, both of point support and nonlinear volume averages. In a simulation mode, the previous cokriging provides the mean and variance of a conditional distribution from which to draw a simulated value, which is then backtransformed into a simulated value of the primary variable. The nonlinear averaged data values are then reproduced exactly. The direct sequential simulation algorithm adopted does not call for using any Gaussian distribution.