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

拟议项目的目标是制定一种方法，对城市地区的航空和高分辨率卫星图像进行有监督的基于背景的分类。主要的科学贡献是开发和应用了用于确定分类模型参数的迁移学习方法，以减少这种模型所需的训练数据量。为了克服仅基于局部上下文的方法的问题，提出了一种层次模型。分类是基于一个数字表面模型和一个真正的正射影像，数学框架提供了条件随机场（CRF）。最近的工作CRF为基础的分类表明，简单的模型，当地的背景可能会导致过度平滑的结果。更复杂的模型可以产生更好的结果，但它们需要大量的训练数据。此外，已知CRF在对场景中的对象之间的远程交互进行建模方面存在问题。为了解决这些问题，我们提出了一种新的基于CRF的分类技术，使用更复杂的上下文模型比可比的方法。为了减少学习这些模型的参数所需的训练数据量，我们的目标是开发适合迁移学习的模型，以便在另一个时间和/或另一个地方获取的训练数据可以被转移到新的场景中进行分类。建议的项目构成了迁移学习原理在图像分析中基于图的分类方法的背景下的首次应用。此外，为了能够在具有现实计算工作量的概率模型中对长程相互作用进行建模，通过构建分层模型来明确地考虑尺度空间。新的方法进行了评价的真实的数据与手动生成的参考。这两个项目涉及迁移学习的不同方面，将交流和比较这两个项目开发的方法。

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.