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A Closed-Form Expression Relating Classification Accuracy to SAR System Calibration Uncertainty

机译:与SAR系统校准不确定性相关的分类精度的闭式表达式

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摘要

The choice of synthetic aperture radar (SAR) system design parameters such as radiometric calibration uncertainty and noise-equivalent sigma zero has a significant impact on applications exploiting SAR image data. However, methods for quantitatively translating the impact of system and mission parameter choices into the application context are lacking. This letter addresses this subject and derives a closed-form algebraic expression for translating radiometric biases due to calibration uncertainties—an important SAR engineering specification—into estimates of classification uncertainties for the restricted case of two homogeneous classes characterized by different backscatter intensity levels. The expression is exploited within this letter to estimate the potential impact of radiometric uncertainty on SAR-derived thematic maps. The results indicate that classification results based on the joint use of data from future SAR constellations in particular may be significantly degraded due to calibration uncertainties.
机译:合成孔径雷达(SAR)系统设计参数的选择,例如辐射定标不确定性和等效噪声的sigma zero(零),对利用SAR图像数据的应用产生了重大影响。但是,缺少将系统和任务参数选择的影响定量地转换为应用程序上下文的方法。这封信解决了这个问题,并得出了一个封闭形式的代数表达式,用于将由于校准不确定性(一种重要的SAR工程规范)而引起的辐射偏差转化为对具有不同反向散射强度水平特征的两个同类类别的受限情况下的分类不确定性的估计。在该信函中使用了该表述,以估算辐射不确定性对SAR衍生主题图的潜在影响。结果表明,由于校准不确定性,基于联合使用未来SAR星座数据的分类结果可能会大大降低。

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