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首页> 外文OA文献 >Working with Gaussian Random Noise for Multi-Sensor Archaeological Prospection: Fusion of Ground Penetrating Radar Depth Slices and Ground Spectral Signatures from 0.00 m to 0.60 m below Ground Surface
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Working with Gaussian Random Noise for Multi-Sensor Archaeological Prospection: Fusion of Ground Penetrating Radar Depth Slices and Ground Spectral Signatures from 0.00 m to 0.60 m below Ground Surface

机译：使用高斯随机噪声进行多传感器考古展望：将地面熔化雷达深度切片的融合，地面光谱签名从地面0.00米到0.60米

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The integration of different remote sensing datasets acquired from optical and radar sensors can improve the overall performance and detection rate for mapping sub-surface archaeological remains. However, data fusion remains a challenge for archaeological prospection studies, since remotely sensed sensors have different instrument principles, operating in different wavelengths. Recent studies have demonstrated that some fusion modelling can be achieved under ideal measurement conditions (e.g., simultaneously measurements in no hazy days) using advance regression models, like those of the nonlinear Bayesian Neural Networks. This paper aims to go a step further and investigate the impact of noise in regression models, between datasets obtained from ground-penetrating radar (GPR) and portable field spectroradiometers. Initially, the GPR measurements provided three depth slices of 20 cm thickness, starting from 0.00 m up to 0.60 m below the ground surface while ground spectral signatures acquired from the spectroradiometer were processed to calculate 13 multispectral and 53 hyperspectral indices. Then, various levels of Gaussian random noise ranging from 0.1 to 0.5 of a normal distribution, with mean 0 and variance 1, were added at both GPR and spectral signatures datasets. Afterward, Bayesian Neural Network regression fitting was applied between the radar (GPR) versus the optical (spectral signatures) datasets. Different regression model strategies were implemented and presented in the paper. The overall results show that fusion with a noise level of up to 0.2 of the normal distribution does not dramatically drop the regression model between the radar and optical datasets (compared to the non-noisy data). Finally, anomalies appearing as strong reflectors in the GPR measurements, continue to provide an obvious contrast even with noisy regression modelling.
机译：从光学和雷达传感器获取的不同遥感数据集的集成可以提高用于映射子表面考古遗骸的整体性能和检测速率。然而，数据融合仍然是考古学锻造研究的挑战，因为远程感测的传感器具有不同的仪器原理，以不同的波长运行。最近的研究表明，使用预先回归模型，可以在理想的测量条件下实现一些融合建模（例如，在没有朦胧的日子中同时测量），如非线性贝叶斯神经网络的那些。本文旨在进一步逐步并调查回归模型中噪声的影响，从地面穿透雷达（GPR）和便携式场光谱分析仪获得的数据集之间。最初，GPR测量提供了三个深度切片，厚度为20厘米，从地面下方0.00 m高达0.60米，而从光谱辐射器获取的地光谱签名被加工以计算13个多光谱和53个高光谱指标。然后，在GPR和光谱签名数据集中加入不同于正常分布的0.1至0.5的高斯随机噪声的各个高斯随机噪声，其平均值为0和方差1。之后，在雷达（GPR）与光学（光谱签名）数据集之间应用贝叶斯神经网络回归拟合。本文实施了不同的回归模型策略。整体结果表明，融合的噪声水平高达0.2的正态分布不会显着地将雷达和光学数据集之间的回归模型（与非噪声数据相比）丢弃。最后，在GPR测量中出现的异常作为强反射器，即使用嘈杂的回归建模也继续提供明显的对比度。

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