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An improvement of multiple-component scattering model with rotated covariance matrix for polarimetric SAR decomposition

机译:旋转协方差矩阵对偏振SAR分解的多分量散射模型的改进

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It has been validated that the cross-polarized scattering (HV) is caused not only by vegetation but also by rotated dihedrals, then the orientation angle of the rotated dihedrals can be obtained by minimizing the cross-polarized scattering component of covariance matrix. Therefore, this paper presents an improvement of multiple-component scattering model of rotated covariance matrix for Polarimetric SAR decomposition in order to detect the rotated buildings. Based on this model, the oriented buildings can be distinguished from the volume scattering mechanism of forest. Comparisons of the multiple-component decompositions with and without rotation of the covariance matrix are conducted using ESAR L-band Polarimetric SAR data of the Oberpfaffenhofen Test Site Area. Experimental results indicate that the improved decomposition model by implementing a rotation of the covariance matrix can recognize the oriented buildings from volume scattering and achieve a better decomposition result and further more accurate interpretation.
机译:已经验证,交叉极化散射(HV)不仅由植被而且通过旋转的Dihedrals引起,然后通过最小化协方差矩阵的交叉极化散射分量来获得旋转Dihedrals的取向角。因此,本文提高了旋转协方差矩阵的多分量散射模型,用于偏振SAR分解,以检测旋转建筑物。基于该模型,可以与森林的体积散射机制区分类。使用OBERPFAFFENHOFEN测试位点的ESAR L波段偏振SAR数据进行具有协方差矩阵的多组分分解的比较。实验结果表明,通过实施协方差矩阵的旋转来改进的分解模型可以从体积散射中识别取向的建筑物,并实现更好的分解结果并进一步更准确的解释。

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