山东半岛近岸海带养殖活动对水体光学吸收特性的影响

为了研究近岸海带养殖活动对水体光学特性的影响,本文选取山东半岛黑泥湾海域为典型研究区,利用2021年4月和8月在该区的9站同步观测数据,分析其总悬浮颗粒物、藻类颗粒物、非藻类颗粒物以及有色溶解有机物的吸收特性,并计算各组分对总吸收系数的贡献率,同时采用Quasi-analytical algorithm (QAA)算法借助MODIS影像反演了水体总吸收系数,结合实测数据获得了黑泥湾水体吸收特性的季节变化情况。结果表明,有海带养殖季节表层水体藻类颗粒物、非藻类颗粒物、总颗粒物和有色溶解有机物的吸收系数明显大于无海带养殖季节,且春季海带养殖区水体的总吸收系数大于无海带养殖区;海底表层沉积物的再悬浮造成底层吸收系数较大,而在春季成熟期的海带汇聚较大量的有机物质导致中层藻类颗粒物吸收系数相对较高;参考波长向红波移动,QAA_v5算法能够较好地估算该区表层水体吸收系数,且在海带养殖季节水体组分会影响到更长的波段特征。本研究可为深入了解海带养殖对水体各组分浓度和分布、碳循环以及水生生态系统的结构和功能的影响提供参照,并有助于提高近岸水体组分的遥感反演精度。     

低通滤波器在高光谱影像分类中的应用

研究了低通滤波器对类别可分性的改善原理,指出将其应用于高光谱影像分类有利于获取更为准确的类别分布信息,进而提高影像分类精度。最后通过实验对低通滤波器在高光谱影像分类中的表现进行了验证。     

一种树型网络拓扑简图的生成算法

给出一种树型网络拓扑的语义描述方法及其拓扑简图的生成算法。该方法通过建立网络联接线路标识串集和网络设备连接标识串集，来构造网络设备的拓扑描述，并计算生成网络拓扑简图。     

A two-dimensional finite volume hydrodynamic model for coastal areas: Model development and validation

A two dimensional implicit finite volume scheme for solving the shallow-water equations is developed. The effects of the Coriolis force, surface wind stress, and waves are included. A non-uniform rectilinear forward staggered grid is used with Cartesian coordinates. The time integration is performed using the Euler implicit technique. The convective flux is treated using the deferred correction method. The viscous terms are discretized using a second order central difference approximation. The SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm is used for coupling the velocity components and the water elevation gradient for the water level correction. The system of equations is solved sequentially using the Strongly Implicit Procedure (SIP). To simulate wave driven current, a phase averaged wave model is used first to simulate wave transformation and calculate radiation stresses. The performance of the developed model is validated for different sources of external forces and different combinations of boundary conditions. The validation cases include tidal circulation in a harbor and wave induced currents behind a breakwater parallel to the coastline. The model is finally applied to simulate the flow pattern in a closed artificial lagoon and along the coastline near Damietta Port located along the Northern coast of Egypt. Results of the developed model agree well with the published results for the considered cases.     

基于合成孔径雷达交叉极化通道数据的海上目标探测

Azimuth ambiguities(ghost targets) discrimination is of great interest with the development of a synthetic aperture radar(SAR). And the azimuth ambiguities are often mistaken as actual targets and cause false alarms. For actual targets, HV channel signals acquired by a fully polarimetric SAR are approximately equal to a VH channel in magnitude and phase, i.e., the reciprocity theorem applies, but shifted in phase about ± for the first-order azimuth ambiguities. Exploiting this physical behavior, the real part of the product of the two cross-polarized channels, i.e.(S S)HV VH, hereafter called A12 r, is employed as a new parameter for a target detection at sea. Compared with other parameters, the contrast of A12 r image between a target and the surrounding sea surface will be obviously increased when A12 r image is processed by mean filtering algorithm. Here, in order to detect target with constant false-alarm rates(CFARs), an analytical expression for the probability density function(pdf) of A12 r is derived based on the complex Wishart-distribution. Because a value of A12 r is greater/less than 0 for real target/its azimuth ambiguities, the first-order azimuth ambiguities can be completely removed by this A12r-based CFAR technology. Experiments accomplished over C-band RADARSAT-2 fully polarimetric imageries confirm the validity.     

SMOS卫星遥感海表盐度资料处理应用研究进展

土壤湿度和海洋盐度卫星首次提供了覆盖全球的高频率、高精度、业务化的海表盐度产品,但其处理和延伸应用仍处于初级阶段,后续校准校正工作还将持续数年,如何及时把握其发展轨迹成为一个重要的科学问题.本研究从SMOS计划、数据概况、盐度反演算法、格点产品制作、多源数据融合和产品应用等方面,介绍和评述了SMOS计划及其海表盐度产品应用研究进展,着重分析了反演算法中的各种误差来源,对在轨2 a的运行情况进行了回顾、对未来的发展重点进行了展望,旨在为开发和应用SMOS产品提供参考.     

基于遗传小波神经网络的海底声学底质识别分类

分割海底声纳探测图像,提取单元特征向量进行主成份分析,选取均值、标准差、对比度、相关系数、能量及同质性作为训练特征向量,构建小波神经网络。利用遗传算法优化小波神经网络的初始权值及小波参数,对砂、礁石、泥3种底质类型分别进行训练,并得到3种底质的测试精度都在90%以上,优于单独利用小波神经网络进行训练时的测试精度,克服了小波神经网络训练时易陷入局部极小的固有缺陷,表明基于遗传算法的小波神经网络可有效用于海底底质声纳图像的识别和分类。     

基于SOM的海表温度遥感数据集的EOF算法重构

针对海表温度数据集的数据缺失,提出了一种基于自组织映射算法(SOM)和经验正交函数算法(EOF)有机结合的重构缺失值的新方法。该方法应用了SOM的非线性估计,能够很好的反映数据集的非线性结构,并把SOM估计的结果用于EOF算法的初始化,克服了EOF对数据集初始化敏感的问题。在处理过程中,对奇异值分解使用了lanczos算子分解矩阵,提高了程序运行效率。此外,该方法还引入蒙特卡罗交叉校正集,确定最佳重构的EOF模态数,最终高精度计算出重构误差。使用AQUA遥感卫星海表温度数据进行实验,结果表明该方法能够很好地重构出缺失率高达83.23%的数据集,且重构精度高。     

Numerical algorithm for the dynamic analysis of base-isolated structures with dry friction

This paper deals with the techniques for solving ordinary differential equations with essential nonlinearity arising from the representation of frictional force by the sign function of the relative velocity. This problem is connected with dynamic behavior studies of base-isolated structures with dry friction devices.Mathematical evidence is presented concerning the inaccuracies of the solution which are connected with the representation of the frictional force by the sign function. An approximation of this sign model is made on the basis of the appropriate linearization of a small part of the frictional force function. A numerical algorithm for the dynamic analysis of base-isolated structures with dry friction is proposed. Numerical tests are carried out for establishing the exactness of the proposed techniques. The obtained results prove the accuracy of the techniques.     

洞庭湖泥沙淤积数值模拟模式

在确保沙量守恒的非恒定流、非均匀沙的二维水沙数学模型的基础上,通过洞庭湖水沙输运和河床变形计算实践,比较了两种冲泻质与床沙质的转化模式,提出了包括冲泻质在内的泥沙淤积模式和水沙动边界计算模式.从洞庭湖泥沙输运和河床变形计算的结果看,洞庭湖泥沙淤积数值模式具有较好的稳定性和较高的模拟精度,说明所建湖泊泥沙模型的良好性能和具有推广运用的前景.