首页> 外文会议>International Geoscience and Remote Sensing Symposium >SYNERGETIC USE OF MULTI-TEMPORAL ALOS PALSAR AND ENVISAT ASAR DATA FOR TOPOGRAPHIC/LAND COVER MAPPING AND MONITORING AT NATIONAL SCALE IN AFRICA
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SYNERGETIC USE OF MULTI-TEMPORAL ALOS PALSAR AND ENVISAT ASAR DATA FOR TOPOGRAPHIC/LAND COVER MAPPING AND MONITORING AT NATIONAL SCALE IN AFRICA

机译:协同造算使用多时间ALOS PALSAR和ENVISAT ASAR数据的地形/陆地覆盖映射和非洲国家规模监测

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The use of Synthetic Aperture Radar (SAR) data in large parts of the African countries, in particular for those close to the equator, is often conditio sine qua non, simply due to the fact that optical data are severely hampered by clouds, especially during the raining (corresponding to the crop) season. The objective of this paper is to present a methodology - and the related results - for the generation of land cover maps and changes over large areas by fusing single-date or interferometric ALOS PALSAR Fine/Dual Beam data with multi-temporal ENVISAT ASAR Image Mode/Alternating Polarization intensity. In synthesis, the method is based on data fusion by exclusively considering - in the prior knowledge-based classifier that requires neither user-defined parameters nor reference samples - the data characteristics and related acquisition modes. Results clearly show that the synergetic use enables the reliable identification of key land cover types (in particular cropped areas, bare soil areas, forestry, forest clear cut, forest burnt areas, water bodies) and their evolution over time, providing basic information on the land cover status. Finally, it is shown that using the same 46-days interferometric ALOS PALSAR data set, a Digital Elevation Model (DEM) with higher quality than the Shuttle Radar Topographic Mission (SRTM) one can be generated in those nearly equatorial - non dense forest - regions.
机译:在非洲国家的大部分地区使用合成孔径雷达(SAR)数据,特别是对于赤道靠近的那些,通常是由Conditio Sine Qua非,这只是由于光学数据严重阻碍了云,特别是在期间下雨(对应于作物)季节。本文的目的是提出一种方法 - 以及相关结果 - 通过用多时间环境envisat ASAR图像模式融合单日或干涉alos Palsar精细/双光束数据,为陆地覆盖映射和大面积变化的变化/交替极化强度。在合成中,该方法通过专门考虑 - 在现有知识的基于分类器中基于数据融合,该分类既不需要用户定义的参数也不需要参考样本 - 数据特征和相关采集模式。结果清楚地表明,协同用途能够可靠地识别关键陆地覆盖类型(特别是裁剪区域,裸土壤区域,林业,森林,森林清除,森林烧焦的地区,水体)及其演变随着时间的推移,提供有关的基本信息陆地覆盖状态。最后,示出了使用相同的46天干涉式Alos Palsar数据集,在几乎赤道 - 非密集森林中可以生成具有比班车雷达地形任务(SRTM)更高的质量的数字高度模型(DEM) - 地区。

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