Kombinierte Objektdetektion und physikalische Modell-Inversion für PolINSAR-Bilddaten

本课题旨在研究目标检测与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。目标检测是指对SAR图像中各种目标类别的实例进行定位，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模型应用于SAR观测值，就特定类别的信息，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。这两种分析方法在遥感研究中都有着悠久而独立的传统。目标检测主要依赖于从给定的可观测物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，物理对象参数估计构成有价值的信息项。同时，这些参数的估计是特定类别的，并且显然受益于准确检测假设的可用性。本文的研究重点是精确地利用这种相互依赖，以获得一个既可以提高目标检测性能又可以提高参数估计精度的反馈循环。

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.