Manifold structure preservative for hyperspectral target detection

A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.     

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C₄ index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.     

一种基于高分辨率距离像自动目标识别新方法

提出了一种基于高分辨率距离像的联合对准与识别新方法。该方法结合功率变换的使用，在利用8米雷达目标实测数据进行的识别实验中，获得了较高的正确识别率。     

神经网络与D-S理论结合的HRR识别研究

基于D-S(Dempster-Shafer)证据理论,比较和研究了相关数据和不相关数据的融合方法,分析了多传感器数据融合的算法:集中式融合算法和分布式融合算法.经过实验证明,执行分布式有反馈融合算法时的效果最好.然后利用该算法,提出了和线性内插神经网络相结合的识别方法.利用4种飞机的步进频率雷达的高分辨率一维距离像,将神经网络的识别结果作为证据分别送入传感器进行融合,进行识别研究.实验证明,与单纯利用神经网络的方法比较,目标的正确识别率得到了改善.     

∑-△型模/数转换方式的研究

模/数转换是控制系统中重要的接口电路,本文对种类繁多的模/数转换电路按照它们的转换方式进行了分类和比较,介绍了模/数转换技术的发展趋势和新兴的∑一△型模/数转换的几个基本概念.     

基于稀疏表示的多雷达信号二维融合处理

多雷达信号融合通过对多视角和多频带雷达信号进行相干融合,可以提高图像的距离和方位向分辨率.为了克服基于谱估计的多雷达信号融合方法稳健性严重依赖于散射点个数估计精度和二维极点配对精度的问题,在深入研究逆合成孔径雷达(ISAR)信号的基础上,构造了多雷达信号二维融合的线性表示模型,将融合处理转化为一个信号表示问题;充分挖掘...     

高分辨率格式与钝舵绕流数值模拟

 采用Beam-Warming/TVD格式，Roe二阶、三阶以及四阶通量差分分裂格式数值计算雷诺平均Navier-Stokes方程，湍流附加粘性采用Baldwin-Lomax模型计算，针对超声速绕钝圆舵流动进行了数值模拟比较研究，计算给出了流场的详细结构，物面压力分布与实验值符合良好，尤其是Roe三阶格式给出了较好的计算结果，两个超声速回流区以及湍流边界层分离也得到了很好的数值模拟。     

阵列高分辨方位估计方法统计性能分析

在简要回顾近年来阵列高分辨定向技术发展的基础上,从算法机理出发,对目前出现的各种高分辨定向算法进行合理分类。重点对一批典型算法的多目标定向性能进行统计分析,并利用统一的仿真模型对它们的估计精度和分辨能力进行比较,结论为：MUSIC,Mini-Norm两种方法的实用性较强,而WSF,IMP,TLS-ESPRIT等方法具有较高的理论研究价值,有待进一步开拓其实用性。