基于单亲遗传算法的SAR图像混合像元分解

已有的遥感影像混合像元分解理论方法都要求遥感影像的通道数目大于地物种类,而合成孔径雷达(SAR)的自身特点决定了SAR图像不可能有过多的通道数目,为解决SAR图像地物种类大于通道数目情况下的混合像元分解问题,本文基于单亲遗传算法提出了一种新的混合像元分解方法,创建了一种新的染色体编码方式及进化迭代方式,新算法很好地实现混合像元的分解,可以分解出比通道数目更多的地物种类.并从北京地区ENVISAT-ASAR图像中截取天安门附近区域作为数据源进行实验,实验结果表明了本文算法的正确性和有效性.     

相向飞行全极化SAR图像对建筑物的立体重构

用多方向飞行的全极化SAR图像可能提取特定三维目标的高度与位置信息,进而实现目标物的几何立体重构。全极化SAR图像数据与单极化SAR相比,可以选择多种极化组合数据,提供对于特定目标几何特征敏感的数据类型,通过多方向飞行SAR图像反演该目标或目标群的高度与位置信息。本文用两幅相向飞行的PI-SAR(日本机载极化与干涉SAR,X波段、1.5m分辨率)图像,提取日本仙台电视塔高度、日本东北大学建筑物群的立体重构。     

局部统计活动轮廓模型的SAR图像海岸线检测

首次将局部统计活动轮廓模型引入SAR图像海岸线检测问题中,提出了一种基于局部统计活动轮廓模型的SAR 图像海岸线检测方法。首先利用C-V模型进行粗分割,消除局部统计活动轮廓模型对初始轮廓线设置要求严格的限制,然后提出了一种基于G⁰分布的局部统计活动轮廓模型,进行精细分割。该模型采用G⁰分布对轮廓线上每一点的邻域进行统计建模,增强了模型数据拟合能力,提高了海岸线检测精度,加入水平集函数惩罚项,消除了重新初始化过程。实测SAR图像实验表明,本文方法可用于精确海岸线检测。     

SAR与TM图像监测耕地被毁情况对比研究

从SAR与LandsatTM成像机理出发,通过对图像像元剖面分析,比较两种图像在监测农村耕地被毁方面的优缺点,探讨土地利用现状监测的有效途径。     

Robust river boundaries extraction of dammed lakes in mountain areas after Wenchuan Earthquake from high resolution SAR images combining local connectivity and ACM

River boundaries extraction from SAR imagery is valuable for flood monitoring and damage assessment. Several rivers, parts of which include dammed lakes caused by landslides and rock avalanches triggered by the 2008 Wenchuan Earthquake, were taken as a case study for robust extraction. In this paper, a novel state-of-the-art approach for automated river boundaries extraction using high resolution synthetic aperture radar (SAR) intensity imagery is presented. The key of our approach lies in the combined usage of local connectivity feature of the river and a region-based active contours model (ACM) in a variational level set framework to differentiate between river and the background. First, sub-patched intensity thresholding segmentation is applied to SAR imagery. Pixels with intensities below the threshold are selected as potential river pixels while the others are potential background pixels. Second, potential river pixels are divided into several connected regions, considering that the river is a big connected region, only relatively bigger regions with similar contrast value are retained as the regions of interest (ROI) while others are noise due to pixel-level decision approach in the first step or shadows due to mountains terrain. Third, the ROI and their contours are regarded as local region and the initial contours to refine the river boundaries, which are used to reduce the scene complexity of ACM and its sensitivity to initial situation, respectively. A novel ACM driven by local image fitting (LIF) energy is presented and used for river boundaries extraction for the first time, which is not only robust against inhomogeneity widely spread in SAR imagery but also can work with efficiency without the need of re-initialization during iteration compared to traditional ACM. The proposed approach was tested on numerous high resolution airborne SAR images containing connected rivers or dammed lakes obtained by Chinese domestic radar system after Wenchuan Earthquake. For the overall dataset, the average commission error, omission error and root mean squared error were 6.5%, 3.3%, and 0.51, respectively. The average computational time for 4000 by 4000 image size was 21 min using a PC-based MATLAB platform. Our experimental results demonstrate that the proposed approach is robust and effective.     

SAR影像窗初始行外方位元素的求解法

本文分析了采用角锥体法确定SAR影像窗外方位元素的局限性,提出了利用星历数据较精确地求解SAR影像窗初始行外方位元素的实用方法,并通过实验证明了该法的正确性。     

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.     

SAR系统不确定性可视化表达及其应用

SAR在成像过程中由于受到外界环境干扰和机载本身的不稳定等不确定因素的影响 ,直接影响 SAR图像的使用。因此 ,对这些不确定因素分析是十分重要的。目前 ,对于 SAR不确定性研究主要是基于统计方法 ,但这种方法不便于揭示 SAR不确定性产生的机理 ,以及不确定性可视化表达。本文首先从机理的角度讨论了 SAR成像系统误差传递函数 ,在确定了 SAR系统误差传递函数之后 ,着重讨论了在误差传递函数基础上利用虚拟现实技术和系统仿真技术进行 SAR成像系统不确定可视化研究 ,最后给出可视化表达的应用实例。     

一种基于数据的核优化新方法

针对核优化问题进行了研究,给出了一种基于数据的智能核优化新方法。算法利用UCI数据和美国实测合成孔径雷达图像数据进行仿真实验,结果验证了该方法的有效性和可行性。     

距离-多普勒法对月面目标定位精度的研究

距离-多普勒(R-D)定位法是利用卫星星历数据和雷达回波数据的距离-多普勒信息对SAR图像目标进行定位,因无须在视场中选参考点,这对月面目标定位具有重要意义。距离-多普勒定位法对地面目标的定位已有广泛的应用,并有较好的精度,本文通过对定位误差源的分析和仿真,得出了距离-多普勒定位法对月面目标定位的精度。