基于高亮特征匹配的双视向SAR图像建筑物高度提取

由于城区场景的复杂性和SAR成像几何畸变的影响,基于单幅SAR图像的建筑物高度提取常常存在很大困难。针对这一问题,利用建筑物目标SAR成像形成的叠掩、二次散射、较强单次散射等散射机制对应的高亮特征非常典型,并且对方向性敏感的特点,提出了一种基于双视向SAR图像高亮特征与几何模型匹配的建筑物高度提取方法。首先分析了建筑物目标的SAR图像散射特征及对雷达视向的敏感性,然后构造了建筑物目标在双视向SAR图像上高亮特征几何模型,然后基于灰度均值、灰度概率分布、边界信息定义匹配函数,并利用多种群遗传算法进行优化求解,最终得到建筑物目标的高度信息。基于模拟和机载SAR图像的试验表明该方法的建筑物高度平均反演误差小于1m,可以有效提高建筑物高度反演的精度。

Building Height Retrieval from Dual\|aspect SAR Images based on Match of Strong Backscattering Features

Building height retrieval from single SAR image is quite difficult due to complexity of urban scene and geometric distortion of SAR imaging system.Aiming at this problem,a new method for building height retrieval was proposed by matching between geometric model and strong backscattering features in dual\|aspect SAR images,because strong backscattering features received by SAR sensor formed by layover,double bounce scattering,and strong odd scattering is very distinct and sensitive to orientation.Scattering characteristics of building in SAR image,as well as its sensitivity to SAR imaging orientation was first analyzed.Then geometric model of strong backscattering features for building in dual\|aspect SAR images was constructed.And then matching function was defined based on backscattering mean,probability density function of backscattering,and boundary information.Finally building height was derived using multi\|population genetic algorithm to optimize matching function.The experiments based on simulated and airborne dual\|aspect SAR images showed that the average error for building height retrieval using the proposed method is smaller than 1 meter,thus it can effectively improve the building height retrieval results from SAR data.