影响测绘相机匹配精度的辐射指标量化

摘要

In consideration of the effect of radiometric specification of a mapping camera on the matching accuracy of imaging, the quantification of main radiometric specifications are analyzed, and the mathematical model between them is developed to provide theoretical foundations for determining the specifications of the mapping camera. Firstly, Least Squares Image Matching Algorithm (LSIM) is deduced mathematically, and then the main factors impacting matching accuracy are presented, including the scene characteristics, Modulation Transfer Function (MTF) for an imaging system, Signal to Noise Ratio (SNR) and Radiometric Distortion (RD) metrics. Then, the image degradation owing to the above factors is modeled and simulated. Based on LSIM, the mathematical model between matching accuracy and radiometric specifications is developed using Genetic Algorithm and Back-propagation Neural Network (GABPNN). Finally, the model is verified using simulated images and on-orbit images. Experimental results indicate that the model precision is less than 0. 01 pixels. It suggests that when the MTFN is set to be greater than 0. 08, SNR greater than 45 and RD less than 4% based on the model, the error of matching accuracy can be less than 0.1 pixel.%考虑测绘相机的辐射指标对影像匹配精度的影响,对相机辐射指标的量化进行研究,建立了辐射指标与影像匹配精度之间的关系模型,为确定测绘相机辐射指标提供理论支持.推导出了最小二乘影像匹配精度的计算模型,提出影响匹配的主要因素,包括成像场景特性、传递函数(MTF)、信噪比(SNR)与辐射畸变(RD)；对上述因素产生的像质退化进行建模与仿真,结合最小二乘匹配以及遗传算法的BP神经网络模型(GABPNN)建立辐射指标与匹配精度之间的数值模型；最后利用仿真与在轨获取的遥感影像数据验证模型.试验结果表明,该模型估算精度＜0.01 pixel.利用该模型在匹配误差＜0.1 pixel的测绘需求下,分析出相机的辐射指标应满足MTFN＞0.08、SNR＞45以及RD＜4％.