抗SPA攻击的快速LSB均衡隐藏算法

提出了一种新的LSB隐藏方法,根据待嵌载体图像像素值的奇偶性和嵌入信息的内容,选择像素值加1或加2的嵌入方式,使得SPA(sample pair analysis)攻击的假设条件在嵌入前后保持平衡,致使SPA攻击失效。在使用该方法嵌入率达到100%时,SPA攻击的估计值仍然小于判决门限。实验不仅验证了新方法抵抗SPA攻击的能力,也验证了新方法可以抵抗RS等检测。新方法嵌入方式简单,且无需在嵌入后再做附加处理,便于实现。     

一种快速全局运动估计算法

为了对全局运动进行准确快速的估计,在对全局运动估计算法进行研究的基础上,提出了一种快速全局运动估计算法。该算法基于非线性密度估计,采用了六参数的仿射模型。为了提高计算速度,采用了3层金字塔进行多分辨率计算,而且在每层迭代计算中,将基于块的外点去除算法与特征点提取算法相结合,这样既加快了算法的速度,又提高了计算结果的准确性。全局运动估计的实验结果表明,该算法在速度和准确性上具有一定的优势。     

Varying-time random effects models for longitudinal data: unmixing and temporal interpolation of remote-sensing data

Remote sensing is a helpful tool for crop monitoring or vegetation-growth estimation at a country or regional scale. However, satellite images generally have to cope with a compromise between the time frequency of observations and their resolution (i.e. pixel size). When concerned with high temporal resolution, we have to work with information on the basis of kilometric pixels, named mixed pixels, that represent aggregated responses of multiple land cover. Disaggreggation or unmixing is then necessary to downscale from the square kilometer to the local dynamic of each theme (crop, wood, meadows, etc.).

Assuming the land use is known, that is to say the proportion of each theme within each mixed pixel, we propose to address the downscaling issue through the generalization of varying-time regression models for longitudinal data and/or functional data by introducing random individual effects. The estimators are built by expanding the mixed pixels trajectories with B-splines functions and maximizing the log-likelihood with a backfitting-ECME algorithm. A BLUP formula allows then to get the ‘best possible’ estimations of the local temporal responses of each crop when observing mixed pixels trajectories. We show that this model has many potential applications in remote sensing, and an interesting one consists of coupling high and low spatial resolution images in order to perform temporal interpolation of high spatial resolution images (20 m), increasing the knowledge on particular crops in very precise locations.

The unmixing and temporal high-resolution interpolation approaches are illustrated on remote-sensing data obtained on the South-Western France during the year 2002.     

Lystematic sampling of two-dimensional surfaces and related problems

The paper is largely concerned with twenty-one possible methods of sampling a plane area, with points as sampling units, for the purpose of estimating a portion of this area having certain defined characteristics. These methods result from a combination, two at a time, of random, stratified and systematic sampling in two perpendicular directions, with or without alignment of the sampled points. Eleven of these methods involve systematic sampling in one or both directions. The estimate of the proportion of the area of interest is simply the proportion of points in the total area, falling within the area having the defined characteristics. For each method the variance function is derived. Fourteen different types of space covariance functions are involved in these variance functions.