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Satellite-based enhancement of archaeological marks through data fusion techniques

机译:通过数据融合技术对卫星进行考古标记的增强

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The application of space technology to archaeological research has been paid great attention worldwide, mainly because the current availability of very high resolution (VHR) satellite imagery, such as, IKONOS (1999) and QuickBird (2001), provide valuable data for searching large areas to find potential archaeological sites. Data from VHR satellite can be very useful for the identification, management and documentation of archaeological resources. Archaeological investigation based on the use of VHR satellite images may take benefits from the integration and synergic use of both panchromatic and multispectral data. This can be achieved by using pansharpening techniques, which allow multispectral and panchromatic images to be merged. The two basic frameworks of pansharpening techniques are Component Substitution (CS), such as Intensity-Hue-Saturation (IHS) Gram-Schmidt (GS), and multiresolution analysis (MRA), such as wavelets and Laplacian pyramids (LP). In this paper, both Gram-Schmidt and Laplacian pyramids with context adaptive (CA) detail injection models were used. QB images were processed for a relevant archaeological area in Southern Italy, the ancient Siris-Heraclea, a very significant test area because it is characterized by the presence of both surface and subsurface ancient remains. Outcomes of different pansharpening techniques have been qualitatively evaluated for both surface and subsurface remains. The visual inspection clearly suggests that the quantitative evaluation of the fusion performance for archaeological applications is a critical issue, and "ad hoc" local (i.e. context-adaptive) indices need to be developed.
机译:空间技术在考古学研究中的应用已引起全世界的广泛关注,这主要是因为诸如IKONOS(1999)和QuickBird(2001)之类的超高分辨率(VHR)卫星图像的当前可用性为搜索大面积区域提供了有价值的数据。寻找潜在的考古遗址。来自VHR卫星的数据对于鉴定,管理和记录考古资源非常有用。基于VHR卫星图像使用的考古调查可能会受益于全色和多光谱数据的整合和协同使用。这可以通过使用锐化技术来实现,该技术可以将多光谱和全色图像合并。锐化技术的两个基本框架是分量替换(CS)(例如强度-色相饱和度(IHS)克-施密特(GS))和多分辨率分析(MRA),例如小波和拉普拉斯金字塔(LP)。在本文中,使用了带有上下文自适应(CA)细节注入模型的Gram-Schmidt和Laplacian金字塔。 QB图像是针对意大利南部一个相关的考古区域,即古老的Siris-Heraclea,这是一个非常重要的测试区域而进行处理的,因为它的特征是表面和地下古老遗迹的存在。对于表面和地下残余物,已经定性评估了不同的全貌处理技术的结果。目视检查清楚地表明,对考古应用的融合性能进行定量评估是一个关键问题,因此需要开发“临时”的局部(即环境适应性)指标。

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