Transfer learning for hierarchical Conditional Random Fields for the classification of urban aerial and satellite images

用于城市航空和卫星图像分类的分层条件随机场的迁移学习

• 批准号: 246463617
• 负责人: Professor Dr.-Ing. Christian Heipke
• 金额: --
• 依托单位: Institut für Photogrammetrie und GeoInformation (IPI)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2013
• 资助国家: 德国
• 起止时间: 2012-12-31 至 2016-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/246463617?language=en
• 关键词: Transfer; learning; hierarchical; Conditional; Random

It is the goal of the proposed project to develop a methodology for the supervised context-based classification of aerial and high-resolution satellite images of urban areas. The main scientific contribution is the development and application of methods for transfer learning for determining the parameters of the classification model in order to reduce the amount of training data required for such a model. In order to overcome problems of methods based on local context only, a hierarchical model is proposed. The classification is based on a digital surface model and a true orthophoto, the mathematical framework is provided by Conditional Random Fields (CRF). Recent work on CRF-based classification has shown that simple models of local context may lead to over-smoothing of the results. More complex models can lead to better results, but they require a considerably larger amount of training data. Furthermore, CRF are known to have problems in modelling long-range interactions between objects in a scene. In order to tackle these problems, we suggest a new CRF-based classification technique using more complex context models than comparable methods. In order to reduce the amount of training data required for learning the parameters of these models, it is our goal to develop models that are suitable for transfer learning, so that training data acquired at another time and/or for another place can be transferred to a new scene to be classified. The suggested project constitutes the first application of the principles of transfer learning in the context of graph-based classification methods in image analysis. Furthermore, in order to be able to model long-range interactions in the probabilistic model with a realistic computational effort, scale space is considered explicitly by building a hierarchical model. The new methodology is evaluated on real data with a reference that was generated manually. The methodology developed in both projects, which deal with different aspects of transfer learning, will be exchanged and compared.

拟议项目的目标是开发一种基于上下文的高分辨率和高分辨率卫星图像的方法论。主要的科学贡献是开发和应用用于确定分类模型参数的转移学习方法，以减少这种模型所需的培训数据量。为了克服仅基于本地环境的方法，提出了层次模型。分类基于数字表面模型和真正的正射手，该数学框架由条件随机场（CRF）提供。关于基于CRF的分类的最新工作表明，局部上下文的简单模型可能会导致结果过度平滑。更复杂的模型可以带来更好的结果，但是它们需要大量的培训数据。此外，众所周知，CRF在模拟场景中对象之间的长距离相互作用时存在问题。为了解决这些问题，我们建议使用比可比方法更复杂的上下文模型的新的基于CRF的分类技术。为了减少学习这些模型参数所需的培训数据量，我们的目标是开发适合转移学习的模型，以便在另一个时间和/或其他地方获得的培训数据可以转移到一个要分类的新场景。建议的项目构成了在图像分析中基于图的分类方法中转移学习原理的第一个应用。此外，为了能够通过逼真的计算工作来模拟概率模型中的远程相互作用，通过构建层次结构模型明确考虑了比例空间。在实际数据上评估新方法，并用手动生成的参考。两个项目中开发的方法将交换和比较。

项目成果

ITERATIVE RE-WEIGHTED INSTANCE TRANSFER FOR DOMAIN ADAPTATION

• DOI: 10.5194/isprs-annals-iii-3-339-2016
• 发表时间: 2016-06
• 期刊: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• 作者: A. Paul;F. Rottensteiner;C. Heipke

TRANSFER LEARNING BASED ON LOGISTIC REGRESSION

• DOI: 10.5194/isprsarchives-xl-3-w3-145-2015
• 发表时间: 2015-08
• 期刊: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• 作者: A. Paul;F. Rottensteiner;C. Heipke

A COMPARISON OF TWO STRATEGIES FOR AVOIDING NEGATIVE TRANSFER IN DOMAIN ADAPTATION BASED ON LOGISTIC REGRESSION

基于逻辑回归的域适应中两种避免负迁移策略的比较

• DOI: 10.5194/isprs-archives-xlii-2-845-2018
• 发表时间: 2018
• 期刊: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• 作者: Rottensteiner;Ostermann;Heipke
• 通讯作者: Heipke