Kombinierte Objektdetektion und physikalische Modell-Inversion für PolINSAR-Bilddaten

该建议涉及的研究，旨在证明在合成孔径雷达（SAR）数据的自动分析中结合目标检测和物理参数估计的优点。目标检测是指在SAR图像中定位各种目标类别的实例，例如道路，建筑物或任何其他类型的土地覆盖。物理参数估计，另一方面，产生一个显着的描述特定类别的信息，如森林生物量，土壤湿度或建筑物高度方面的特定实例，通过应用一个给定的模型SAR观测。这两种分析在遥感研究中有着悠久但基本上独立的传统。目标检测关键依赖于从给定的可观测量中提取有意义的特征，以解决使决策过程复杂化的模糊性;物理目标参数估计在此背景下构成有价值的信息项。这些参数的估计，在同一时间，是特定类别的，显然受益于准确的检测假设的可用性。所提出的研究重点是利用正是这种相互依赖性，以获得一个反馈周期，在该周期中，目标检测性能和参数估计精度都可以提高。

This proposal concerns research intended to demonstrate the merits of combining object detection and physical parameter estimation in the automatic analysis of Synthetic Aperture Radar (SAR) data. Object detection refers to the localization of instances of various object categories, such as roads, buildings or any other type of land cover, in SAR images. Physical parameter estimation, on the other hand, yields a salient description of a given instance in terms of category specific information such as forest biomass, soil moisture or building height by applying a given model to SAR observables. Both kinds of analysis have a long but largely independent tradition in remote sensing research. Object detection relies crucially on the extraction of meaningful features from given observables to resolve ambiguities that complicate the decision process; physical object parameter estimates constitute valuable items of information in this context. The estimation of these parameters, at the same time, is category specific and clearly profits from the availability of accurate detection hypotheses. The proposed research focuses on exploiting precisely this mutual dependence to obtain a feedback cycle in which both object detection performance and parameter estimation accuracy can be improved.