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改进的极化SAR图像三分量分解方法 被引量:2

Improved Three-Component Scattering Power Decomposition for Polarimetric SAR Image
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摘要 针对原始三分量分解方法中,偶次散射和表面散射类地物的体散射分量可能会过估计的问题,提出了一种基于H/α珔参数和一般散射机理模型的改进的三分量分解方法。首先在进行极化分解之前利用H/α珔参数判定目标的主导散射类型。对于主导散射类型明确的像素点,利用一般散射机理模型优先计算主导散射成分的相关扩展系数,然后再从剩余成分中提取非主导散射分量。对于主导散射类型不明确的像素点,采用传统的去取向三分量分解方法进行分解。对实测极化SAR图像进行实验表明,新方法不仅继承了传统三分量分解方法的优点,而且能在分解结果中更加有效地突出雷达目标的主导散射特征。 To solve the problem of overestimation of volume scattering power.for the pixels dominated by surface or double bounce scattering, an improved three-component scattering power decomposition method based on the scattering characteristic parameters H/α and the general scattering mechanism models is proposed in this paper. First, the dominant scattering type of radar targets is identified by using parameters H and a before decomposition. For the pixeles whose dominant scattering mechanism can be explicitly confirmed, the best fit expansion coefficients are extracted of the dominating contribution is first computed by using the general scattering mechanism models, and subsequently the residual components from the remainder. While for the pixels whose dominant scattering type is hard to determine, the traditional three-component scattering power decomposition with deorientation process is used. By applying the new decomposition scheme to real fully polarimetric SAR image, it is shown that the new method not only inherits the advantages of the traditional three-component scattering power decomposition but also gives prominence to the radar targets characterized by strong volume, surface and double bounce scatterings.
出处 《宇航学报》 EI CAS CSCD 北大核心 2013年第7期980-986,共7页 Journal of Astronautics
关键词 SAR 极化SAR 极化目标分解 散射机理模型 三分量分解 SAR Polarimetric SAR Polarimetric target decomposition Scattering mechanism model Three-component scattering power decomposition
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参考文献12

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  • 5Wu Bingfang, Li Qiangzi. Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes[J]. International Journal of Applied Earth Observation and Geo-information, 2012, 16(2012): 101- 112.
  • 6Zhu Z, Woodcock C E, Rogan J, et al. Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data[J]. Remote Sensing of Environment, 2012, 117: 72-82.
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  • 9An Wentao, Cui Yi, Yang Jian. Three-component model-based decomposition for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(6): 2732-2739.
  • 10Mishra P, Singh D, Yamaguchi Y. Land cover classification of PALSAR images by knowledge based decision tree classifier and supervised classifiers based on SAR observables[J]. Progress in Electromagnetics Research-Pier, 2011, 30(4): 47-70.

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