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首页> 外文会议>The 28th International Symposium on Remote Sensing of Environment, Mar 27-31, 2000, Cape Town, South Africa >ERS/JERS-SAR Multitemporal Data Application for Russian Boreal Forests Regional Monitoring - Case Study For Hot-Spot Area in North-Western Russia
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ERS/JERS-SAR Multitemporal Data Application for Russian Boreal Forests Regional Monitoring - Case Study For Hot-Spot Area in North-Western Russia

机译：ERS / JERS-SAR多时相数据在俄罗斯北方森林区域监测中的应用-以俄罗斯西北部热点地区为例

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摘要

This work is dedicated to the assessment of a possibility of using ERS/JERS-SAR multitemporal data in regional forest monitoring. This study is carried out for test area in North-Western Russia where significant variations in the forest cover take place. In the present study the methodical approach to process SAR images is developed based on the Kingisepp test site (North-Western Russia). This is dictated by the availability of detailed ground truth data on this site. The study is based on methods of supervised and unsupervised classification, as well as the developed algorithm of automatic classification with several texture parameters included. Based on the present research the following conclusion can be drawn: the use of texture in SAR images analyses can significantly improve the classification accuracy compared to an analysis based on the mean backscatter value alone; the accuracy of classification of JERS1 SAR images is higher than that of classification of ERS1,2 SAR images for the majority of the forest types investigated.
机译：这项工作致力于评估在区域森林监测中使用ERS / JERS-SAR多时相数据的可能性。这项研究是针对俄罗斯西北部的森林覆盖率发生较大变化的测试区域进行的。在本研究中，基于Kingisepp测试站点（俄罗斯西北部）开发了处理SAR图像的方法。这取决于该站点上详细的地面真实数据的可用性。该研究基于有监督和无监督分类方法，以及所开发的具有多个纹理参数的自动分类算法。基于本研究，可以得出以下结论：与仅基于平均后向散射值的分析相比，在SAR图像分析中使用纹理可以显着提高分类精度；对于大多数被调查森林类型，JERS1 SAR图像分类的准确性高于ERS1,2 SAR图像分类的准确性。

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