基于Otsu和FMM方法的风洞试验图像修复

各种光学测试技术在风洞试验中应用越来越广泛，图像中的破损或无关信息均会影响数据的处理和分析。以存在缺陷的风洞纹影图像为例，对数字图像修复技术进行了研究。根据缺陷离散分布、大小及形状各异，核心区域的灰度值较低以及边缘处灰度梯度变化较大等特点，将图像修复流程设计为确定待处理区域、缺陷识别和缺陷修复等3个步骤。前2个步骤使用Otsu算法进行图像的分割处理，然后使用快速行进方法（FMM）完成缺陷修复。经处理，纹影图像的缺陷被自动识别，并得到了减少或削弱，而且修复的信息客观合理。该方法可推广至其他光学测试技术的图像修复。     

A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data

The current paper introduces a new multilayer perceptron (MLP) and support vector machine (SVM) based approach to improve daily rainfall estimation from the Meteosat Second Generation (MSG) data. In this study, the precipitation is first detected and classified into convective and stratiform rain by two MLP models, and then four multi-class SVM algorithms were used for daily rainfall estimation. Relevant spectral and textural input features of the developed algorithms were derived from the spectral MSG SEVIRI radiometer channels. The models were trained using radar rainfall data set colected over north Algeria. Validation of the proposed daily rainfall estimation technique was performed by rain gauge network data set recorded over north Algeria. Thus, several statistical scores were calculated, such as correlation coefficient (r), root mean square error (RMSE), mean error (Bias), and mean absolute error (MAE). The findings given by: (r = 0.97, bias = 0.31 mm, RMSE = 2.20 mm and MAE = 1.07 mm), showed a quite satisfactory relationship between the estimation and the respective observed daily precipitation. Moreover, the comparison of the results with those of two advanced techniques based on random forests (RF) and weighted ‘k’ nearest neighbor (WkNN) showed higher accuracy obtained by the proposed model.     

Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other.     

地基光学图像中本体-帆板结构航天器姿态估计方法

提出一种利用地基光学图像估计空间目标姿态的方法。针对本体-帆板结构航天器外形结构特点，定义了一种不易遮挡的两轴结构特征，设计了地基光学图像中该特征的提取方法；根据航天器成像过程建立了特征点二三维几何投影方程，实现了航天器三维姿态的解算。利用单站序列图像，可获得航天器姿态变化角速度信息。在轨航天器的试验结果表明，本方法能有效地从地基光学图像估计出航天器姿态。     

基于颜色、形状和纹理的多特征融合图像检索

基于单一特征的图像检索的许多方法都已经相当成熟,如何有效利用这些特征是基于内容图像检索的一个关键问题。利用颜色、纹理、形状等特征,在多特征融合检索时用动态生成权值的方法进行图像检索。实验结果表明,算法能够准确、高效地检索出目标图像,相对于单一特征的检索,有效提高了图像的检索精度。     

弹道导弹中段目标/背景红外图像建模与仿真

提出了一种针对弹道导弹中段目标和背景的动态红外辐射特性仿真方法。首先,建立了弹头目标^秀饵的三维模型,并结合其运动位置分析了影响弹头目标／诱饵红外辐射的因素,建立了较为完善的弹头目标/诱饵红外物理模型;其次,基于红外星表数据对恒星大气层外的辐照度进行了计算,根据红外传感器视轴指向和红外传感器视场,确定了红外传感器视场中的可见恒星,并根据拟生成背景图像的空间分辨率计算出这些恒星在背景图像中的相对位置分布,对星空背景直接进行了动态图像生成;然后,对成像过程中红外探测系统的噪声效应进行了建模与分析,同时给出了噪声的计算机模拟方法;最后,将生成的弹头目标,诱饵的红外图像与星空背景红外图像在灰度和空间2个层次上进行合成形成完整的弹头目标赝饵／星空背景的红外仿真图像,可为弹道导弹中段红外电子对抗突防提供较为准确的数据支持。     

基于深度学习的SAR图像质量提升方法研究

提升合成孔径雷达(SAR)图像质量以增强其可判读性,一直是SAR图像处理中的关键问题。近年来,深度学习在光学图像处理中取得显著的成功,并逐步应用到SAR图像质量提升领域。对深度学习在SAR图像质量提升中的关键应用进行综述,对深度学习在SAR图像质量提升中采用的典型网络进行了介绍,并从SAR图像旁瓣抑制、超分辨(SR)处理和图像融合3个方面对深度学习的应用进行阐述。最后,分析与探讨了基于深度学习的SAR图像质量提升中的关键问题及进一步研究方向。     

基于旋转双光楔的像方扫描大视场成像光学系统设计

针对无人机传统外挂式光电载荷吊舱窗口尺寸大、无法实现大视场扫描需求的现状，提出了一种基于旋转双光楔的像方扫描红外大视场成像光学系统设计。该系统在传统像方扫描光学镜组基础上引入了双光楔扫描器，以增大系统的扫描视场角，解决了高速无人机目前面临的红外窗口有限且成像视场角小的问题。采用光学设计软件进行了仿真，设计后的光学系统物方视场范围为±21.665°，满足了大视场范围的工作要求。设计结果表明，在工作波段（3.7μm ~4.8μm）范围内，该系统的成像质量高，中心视场调制传递函数在33(lp/mm)空间频率下可达0.45，全视场范围内的调制传递函数>0.42@33(lp/mm)，像点能量集中度较好，接近衍射极限。与传统扫描光学系统相比，采用基于旋转双光楔的新型像方扫描成像光学系统设计，在保证各项设计指标不变的前提下，能够将光学系统的径向尺寸减少20%以上，可满足未来无人机实现轻量化和小型化的应用需求，拥有良好的应用前景。     

飞行员头部特征分析及头型分类方法研究

通过对飞行员进行实地调查,发现目前装备的飞行头盔舒适度并不理想。由此,本文介绍了一种新的头部分析方案,借助光栅投影图像测量技术构建的飞行员头部三维模型,通过数值拟合方法分析获得飞行员的头部特征外形参数。同时按照统计学原理,根据头高、头宽、头长尺寸对飞行员的头型进行了科学地分类。本文描述的外形参数分析方法和头型分类方法可以用在今后大规模飞行员头部特征测量分析以及改进头盔设计的工作中。相关标准对今后头盔设计的改进工作有参考意义。     

基于数字图像处理的温度场测量方法的研究

通过对示温漆的颜色温度特性进行分析,给出了一种基于示温漆的彩色图像处理和温度识别方案.该方法具有成本低、精度高等优点,具有工业推广价值.