Andrea Baraldi, Michael Laurence Humber, Dirk Tiede, Stefan Lang
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To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (<i>Val</i>) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2-Validation-accomplishes a GEO-CEOS stage 4 <i>Val</i> of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. 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引用次数: 7
摘要
欧空局将经大气、邻接和地形效应校正的多光谱(MS)图像与其数据衍生的场景分类图(SCM)叠加在一起定义为地球观测(EO) 2级信息产品,其图例包括云和云阴影的质量层。目前还没有在地面段系统地生成ESA EO 2级产品。为了填补从EO大数据到ESA EO 2级产品的信息空白,符合GEO-CEOS阶段4验证(Val)指南,选择了一个现成的卫星图像自动成图器(SIAM)轻型计算机程序,通过独立手段对2006年至2009年美国(CONUS)年度30米分辨率Web-Enabled Landsat data (WELD)图像合成时间序列进行验证。SIAM核心是一种基于先验知识的决策树,用于将MS反射空间超多面体化为静态(非自适应数据)颜色名称。为了便于阅读,本文分为两部分。目前的第2部分验证完成了geo - ceo阶段4的测试SIAM-WELD年度地图时间序列,并与参考的30米分辨率16级USGS国家土地覆盖数据(NLCD) 2006地图进行了比较。这些测试和参考地图对具有相同的空间分辨率和空间范围,但它们的图例不同,必须协调一致,与前面的第一部分-理论一致。结论是SIAM系统地提供了ESA EO 2级SCM产品实例,其图例符合标准的2级4级粮农组织土地覆盖分类系统(LCCS)二分类阶段(DP)分类法。
GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 2: Validation.
ESA defines as Earth Observation (EO) Level 2 information product a multi-spectral (MS) image corrected for atmospheric, adjacency, and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2-Validation-accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. These test and reference map pairs feature the same spatial resolution and spatial extent, but their legends differ and must be harmonized, in agreement with the previous Part 1 - Theory. Conclusions are that SIAM systematically delivers an ESA EO Level 2 SCM product instantiation whose legend complies with the standard 2-level 4-class FAO Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy.