基于波动方程的聚焦点控制照明叠前深度偏移

 基于波动方程的聚焦点控制照明叠前深度偏移技术借助于差分计算，把速度、密度等介质参数的影响体现在差分计算的矩阵方程中，能够自动适应速度场的任意变化，快速傅里叶变换的使用也加速了波场延拓的计算速度。因此此法兼具有限差分偏移方法和傅里叶偏移方法的优点，既可适应速度场的剧烈变化，又可保证对陡倾地层的成像效果，是目前针对复杂构造最有效的成像方法之一。对于单个聚焦点及其周围的成像步骤为：①采用矩形网格情况下绕射走时的有限差分计算方法生成合成算子；②应用合成算子来合成面炮震源和面波记录；③对合成的面炮震源和面波记录做傅里叶有限差分法波动方程叠前深度偏移，得到该聚焦点及其附近区域的成像结果。按照上述成像步骤，将震源波场和炮集记录依据相应的外推公式进行延拓，最终应用成像条件求取成像值。在地质目标处选取多个聚焦点，可以得到面向目标的控制照明偏移成像，在多个层位上选取多个聚焦点进行控制照明叠前深度偏移，可以得到整块区域的成像。通过对Marmousi模型的试算，取得了较好的效果。     

基于实时地形校正的InSAR图像匹配算法

针对侧视雷达/可见光图像匹配制导系统中由于雷达图像地形畸变引起的误匹配问题,提出了一种基于干涉合成孔径雷达（InSAR）的实时地形校正图像匹配算法。该算法以侧视雷达成像几何构象为基础,利用InSAR获取的实时地形数据对获取的SAR景象数据进行实时几何校正,生成无畸变的SAR景象数据,然后利用校正后的SAR景象数据与提前安装的可见光基准数据进行基于去均值归一化互相关模板的图像匹配。实验结果表明,通过实时地形校正,该景象匹配算法在复杂地形区域的匹配概率和匹配精度都大大优于传统SAR景象匹配算法,有效地提高了SAR图像匹配制导技术的适用性。     

Compressive sensing SAR range compression with chirp scaling principle

Compressive sensing(CS) techniques offer a framework for the detection and allocation of sparse signal with a reduced number of measurements.This paper proposes a novel SAR range compression,namely compressive sensing with chirp scaling(CS-CS),achieving the same range resolution as conventional SAR approach,while using fewer range samplings.In order to realize accurate range cell migration correction(RCMC),chirp scaling principle is used to construct reference matrix for compressive sensing recovery.Additionally,error diagrams are designed for measurement of the performance of CS-CS,and some experiments of using real data are performed to deal with the errors caused by three conditions:SNR,sparsity and sampling.     

Multi-channel SAR imaging based on distributed compressive sensing

The rapid development of compressive sensing(CS)shows that it is possible to recover a sparse signal from very limited measurements.Synthetic aperture radar(SAR)imaging based on CS can reconstruct the target scene with a reduced number of collected samples by solving an optimization problem.For multi-channel SAR imaging based on CS,each channel requires sufficient samples for separate imaging and the total number of samples could still be large.We propose an imaging algorithm based on distributed compressive sensing(DCS)that reconstructs scenes jointly under multiple channels.Multi-channel SAR imaging based on DCS not only exploits the sparsity of the target scene,but also exploits the correlation among channels.It requires significantly fewer samples than multi-channel SAR imaging based on CS.If multiple channels offer different sampling rates,DCS joint processing can reconstruct target scenes with a much more flexible allocation of the number of measurements offered by each channel than that used in separate CS processing.     

遥感图像理想均衡化及图像质量定量评价

大多数原始的遥感影像由于其灰度分布集中在较窄的范围内,影像的细节不够清晰,对比度较低。为了使影像的灰度范围拉开或使灰度均匀分布,从而增大反差,增强影像细节信息,通常采用的方法为直方图均衡化。通过对信息熵定义的阐述,引出直方图均衡化的图像增强算法。通过分析传统直方图均衡化算法中存在的缺陷,进而基于分段映射思想提出一种改进的理想直方图均衡化算法。同时,为了对传统算法和改进算法进行定量化地分析比较,基于同时对比度以及人类视觉对比度分辨率限制和模糊数学的相关思想,分别提出基于加权几何平均数法的合成平均对比度和细节评价参数的定义。最后,采用同时对比度、基于加权几何平均数法的合成平均对比度以及细节评价参数作为定量评价的指标,对所提出的改进算法进行了定量评价。评价结果表明,该改进算法的图像增强效果优于传统的直方图均衡化算法。     

A Web Server for Designing Molecular Switches Composed of Two Interacting RNAs

The programmability of RNA–RNA interactions through intermolecular base-pairing has been successfully exploited to design a variety of RNA devices that artificially regulate gene expression. An in silico design for interacting structured RNA sequences that satisfies multiple design criteria becomes a complex multi-objective problem. Although multi-objective optimization is a powerful technique that explores a vast solution space without empirical weights between design objectives, to date, no web service for multi-objective design of RNA switches that utilizes RNA–RNA interaction has been proposed. We developed a web server, which is based on a multi-objective design algorithm called MODENA, to design two interacting RNAs that form a complex in silico. By predicting the secondary structures with RactIP during the design process, we can design RNAs that form a joint secondary structure with an external pseudoknot. The energy barrier upon the complex formation is modeled by an interaction seed that is optimized in the design algorithm. We benchmarked the RNA switch design approaches (MODENA+RactIP and MODENA+RNAcofold) for the target structures based on natural RNA-RNA interactions. As a result, MODENA+RactIP showed high design performance for the benchmark datasets.