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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Improving Forest Height Retrieval by Reducing the Ambiguity of Volume-Only Coherence Using Multi-Baseline PolInSAR Data
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Improving Forest Height Retrieval by Reducing the Ambiguity of Volume-Only Coherence Using Multi-Baseline PolInSAR Data

机译：通过使用多基线PolInSAR数据降低仅体积相干性的歧义性来改善森林高度检索

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Nonvolume decorrelation ($gamma _{mathrm {Nonvol}}$ ) united with the unknown ground contribution will bring a 2-D ambiguity to volume-only coherence, making the inversion underdetermined even when multiple baselines are available. In the context of random volume over ground (RVoG) model and three-stage algorithm, this paper theoretically presented the varied response of different baselines to both $gamma _{mathrm {Nonvol}}$ and ground contribution, and then proposed a new multi-baseline inversion method to reduce the 2-D ambiguity. The proposed method includes two steps, calculating the common overlapped ambiguity from different baselines and fixing the extinction coefficient, to more accurately retrieve the volume-only coherence and forest height. It makes no assumptions on $gamma _{mathrm {Nonvol}}$ and ground contribution. The method was validated and compared with three single-baseline inversions and two published multi-baseline inversions by using the airborne P-band polarimetric SAR interferometry (PolInSAR) data and the reference data of LiDAR canopy height model (CHM) over a dense rainforest site. Results showed that the developed multi-baseline method successfully reduced the combined influence of both $gamma _{mathrm {Nonvol}}$ and ground contribution, and performed better than any single baseline, improving the ${R}{2}$ from 0.60 to 0.77 and unbiased root-mean-square error (RMSE) from 1.32 to 1.04 m at the scale of ca. $100 imes 140,,ext{m}{2}$ . Moreover, the multi-baseline scheme is relatively robust among different baseline combinations.
机译：结合未知地面贡献的非体积解相关（$ gamma _ { mathrm {Nonvol}} $）将为仅体积的相干性带来2D模糊性，即使在有多个基线的情况下，反演也不确定。在地面随机体积（RVoG）模型和三阶段算法的背景下，本文从理论上提出了不同基线对$ gamma _ { mathrm {Nonvol}} $和地面贡献的变化响应，然后提出了新的多基线反演方法可降低二维模糊度。所提出的方法包括两个步骤，从不同的基线计算常见的重叠歧义并固定消光系数，以更准确地检索仅体积的连贯性和森林高度。它不对$ gamma _ { mathrm {Nonvol}} $和地面贡献量做任何假设。通过使用机载P波段极化SAR干涉测量（PolInSAR）数据和茂密的雨林站点上的LiDAR冠层高度模型（CHM）的参考数据，对该方法进行了验证并与三个单基线反演和两个已发布的多基线反演进行了比较。 。结果表明，开发的多基线方法成功地降低了$ gamma _ { mathrm {Nonvol}} $和地面贡献的综合影响，并且比任何单个基线都有更好的表现，从而提高了$ {R} {2} $范围从0.60到0.77，无偏均方根误差（RMSE）从1.32到1.04 m（在ca范围内）。 $ 100 times 140 ，， text {m} {2} $。此外，多基线方案在不同的基线组合之间相对稳健。

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