Kombinierte Objektdetektion und physikalische Modell-Inversion für PolINSAR-Bilddaten

这项建议涉及的研究旨在证明目标检测和物理参数估计相结合在合成孔径雷达(SAR)数据自动分析中的优点。目标检测是指在合成孔径雷达图像中定位各种目标类别的实例，如道路、建筑物或任何其他类型的土地覆盖。另一方面，物理参数估计通过将给定的模型应用于合成孔径雷达观测值，根据特定类别的信息，如森林生物量、土壤湿度或建筑物高度，产生对给定实例的显著描述。这两种分析在遥感研究中都有着悠久但基本上独立的传统。物体检测主要依赖于从给定的观测数据中提取有意义的特征，以解决使决策过程复杂化的模糊性；在这种情况下，物理物体参数估计是有价值的信息项。同时，这些参数的估计是特定类别的，显然得益于准确的检测假设的可用性。所提出的研究集中于准确地利用这种相互依赖来获得能够同时提高目标检测性能和参数估计精度的反馈周期。

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.