TomoSAR II: 3-D Semantic Scene Interpretation of Tomographic SAR Data

TomoSAR II：层析 SAR 数据的 3-D 语义场景解释

• 批准号: 273687959
• 负责人: Dr. Olivier D' Hondt, Ph.D.
• 金额: --
• 依托单位: Fachgebiet Computer Vision and Remote Sensing
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/273687959?language=en
• 关键词: TomoSAR; II; Semantic; Scene; Interpretation

This project is a follow-up to our previous project entitled “Information extraction methods for Tomographic Synthetic Aperture Radar (TomoSAR) data”. Our objective is to focus on the development of algorithms to obtain an automatic semantic interpretation of the 3-D information contained in tomographic SAR data.To achieve this goal, we will first define a 3-D supervised classification method for tomograms exploiting rich 3-D descriptors, combined with the result of previously established 2-D image classification. This classifier will be adapted to recognize “continuous” objects such as forest, grassland, street and field. To identify “discrete” objects such as buildings and other man-made structures, we will propose two complementary types of detectors. The first one called “perceptual grouping” will consider the grouping of geometric primitives thanks to features inspired from the Gestalt psycho-visual theory. The second one is aimed at localizing and estimating the 3D pose of objects thanks to high-level models describing groups of keypoint features. Our new detection approaches will rely on the generation of an object database obtained thanks to the development of an adapted TomoSAR simulation method, addressing the lack of training data for SAR. All the information from these classifier and detectors will be combined into a single scene interpretation framework thanks to a cost-based formulation. This representation will be exploited in a feedback loop to refine the tomograms and the quality of individual object extraction. The loop will be closed by updating the semantic interpretation from the outputs of the previous steps.Additionally, we will continue to work on spatially adaptive covariance estimation to solve the problems of single-look data filtering and address the issue of model order estimation for filtered data. These steps will be integrated into a TomoSAR processing chain leading to our final scene interpretation.

该项目是我们先前题为“层析合成孔径雷达数据的信息提取方法”的项目的后续项目。我们的目标是专注于开发算法来获得断层SAR数据中包含的3D信息的自动语义解释。为了实现这一目标，我们将首先利用丰富的3D描述符定义一种针对断层图像的3D监督分类方法，结合先前建立的2D图像分类结果。该分类器将适用于识别“连续”对象，如森林，草地，街道和领域。为了识别“离散”物体，如建筑物和其他人造结构，我们将提出两种互补类型的探测器。第一个被称为“感知分组”将考虑几何图元的分组，这要归功于从完形心理视觉理论中获得灵感的特征。第二个目标旨在通过描述关键点特征组的高级模型来定位和估计对象的3D姿态。我们的新的检测方法将依赖于生成的对象数据库获得的一个适应TomoSAR模拟方法的发展，解决SAR训练数据的缺乏。所有来自这些分类器和检测器的信息将被组合成一个单一的场景解释框架，这要归功于一个基于成本的公式。该表示将在反馈回路中被利用，以改进断层图像和单个对象提取的质量。此外，我们将继续研究空间自适应协方差估计，以解决单视数据滤波问题，并解决滤波数据的模型阶估计问题。这些步骤将被集成到TomoSAR处理链中，导致我们最终的场景解释。