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首页> 外文期刊>Current Science: A Fortnightly Journal of Research >Fusion of complementary information of SAR and optical data for forest cover mapping using random forest algorithm
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Fusion of complementary information of SAR and optical data for forest cover mapping using random forest algorithm

机译：用随机林算法融合SAR和光学数据的互补信息

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摘要

We developed a methodological framework for accurate forest cover mapping of Shivamogga taluk, Karnataka, India using multi-sensor remote sensing data. For this, we used Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data. These datasets were fused using principal component analysis technique, and forest and non-forest areas were classified using a random forest (RF) algorithm. Backscatter analysis was performed to understand the variation in gamma(0) values between forest and non-forest sample points. The average gamma(0) values of forest were higher than the non-forest samples in VH and VV polarizations. The average gamma(0) backscatter difference between forest and non-forest samples was 8.50 dB in VH and 5.64 dB in VV polarization. The highest classification accuracy of 92.25% was achieved with the multi-sensor fused data compared to the single-sensor SAR (78.75%) and optical (83.10%) data. This study demonstrates that RF classification of multi-sensor data fusion improves the classification accuracy by 13.50% and 9.15%, compared to SAR and optical data.
机译：我们利用多传感器遥感数据，为印度卡纳塔克邦Shivamogga taluk的精确森林覆盖制图制定了方法框架。为此，我们使用了Sentinel-1合成孔径雷达（SAR）和Sentinel-2光学数据。使用主成分分析技术对这些数据集进行融合，并使用随机森林（RF）算法对森林和非森林区域进行分类。进行后向散射分析，以了解森林和非森林采样点之间γ（0）值的变化。在VH和VV极化中，森林样品的平均伽马（0）值高于非森林样品。森林和非森林样品的平均伽马（0）后向散射差异在VH中为8.50 dB，在VV极化中为5.64 dB。与单传感器SAR（78.75%）和光学（83.10%）数据相比，多传感器融合数据的分类准确率最高，达到92.25%。本研究表明，与SAR和光学数据相比，多传感器数据融合的射频分类提高了13.50%和9.15%的分类精度。

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