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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >A maximum-likelihood estimator to simultaneously unwrap, geocode, and fuse SAR interferograms from different viewing geometries into one digital elevation model
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A maximum-likelihood estimator to simultaneously unwrap, geocode, and fuse SAR interferograms from different viewing geometries into one digital elevation model

机译：一种最大似然估计器，可同时将来自不同观察几何体的SAR干涉图解包，地理编码和融合到一个数字高程模型中

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摘要

This paper presents theory, algorithm, and results of a maximum-likelihood algorithm that is capable to fuse a number of heterogeneous synthetic aperture radar interferograms into a single digital elevation model (DEM) without the need for the critical phase-unwrapping step. The fusion process takes place in the object space, i.e., the map geometry, and considers the periodic likelihood function of each individual interferometric phase sample. The interferograms may vary regarding their radar wavelength, their baseline, their heading angle (ascending or descending), and their incidence angle. Geometric baseline error estimates and a priori knowledge from other estimates like existing DEMs are incorporated seamlessly into the estimation process. The presented approach significantly differs from the standard DEM generation method where each interferogram is first phase-unwrapped individually, then geocoded into a common map geometry, and finally averaged with DEMs generated from other interferograms. By avoiding the phase-unwrapping step, the proposed algorithm does not depend on gradients between samples and is therefore capable to reconstruct the arbitrary height of each single scatterer. Because the height of each DEM sample is determined individually, spatial propagation of phase-unwrapping errors is avoided. The algorithm is targeted to fuse an ensemble of interferometric multiangle or multibaseline observations in areas of rugged terrain or highly ambiguous data where algorithms based on phase unwrapping may fail. The algorithm is explained, and examples with real data from the Shuttle Radar Topography Mission are given. Conditions of future missions are simulated, and optimization criteria for the viewing geometry are discussed.
机译：本文介绍了最大似然算法的理论，算法和结果，该算法能够将多个异类合成孔径雷达干涉图融合到单个数字高程模型（DEM）中，而无需进行关键的相位展开步骤。融合过程发生在对象空间即地图几何中，并考虑了每个单独的干涉相位样本的周期性似然函数。干涉图可能会因雷达波长，基线，航向角（上升或下降）以及入射角而异。几何基线误差估计和来自其他估计（例如现有DEM）的先验知识被无缝地合并到估计过程中。所提出的方法与标准的DEM生成方法有很大的不同，在标准的DEM生成方法中，每个干涉图首先分别进行相位展开，然后地理编码为通用的地图几何图形，最后使用从其他干涉图生成的DEM进行平均。通过避免相位解缠步骤，所提出的算法不依赖于样本之间的梯度，因此能够重构每个单个散射体的任意高度。由于每个DEM样本的高度都是单独确定的，因此可以避免相位展开误差的空间传播。该算法的目的是在崎terrain的地形或高度模糊的数据区域融合干涉式多角度或多基线观测结果，而在这些区域中基于相位展开的算法可能会失败。对该算法进行了解释，并给出了具有航天飞机雷达地形任务的真实数据的示例。模拟了未来任务的条件，并讨论了查看几何的优化标准。

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