Inductive transfer-learning for the classification of aerial and satellite images using Bayesian methods

高分辨率航空和卫星图像是遥感中最重要的空间信息来源。使用监督学习算法对这些图像进行分类是图像处理和解释的基本步骤。这需要一个训练样本，必须在一个艰苦的过程中精心准备。在这个过程中，一种可能有助于减少手工工作的范例被称为迁移学习。一个强大的迁移模型允许我们重用来自类似数据集的训练样本，这些数据集与我们的目标图像在空间（近距离）或时间（多时间图像）上相关。关键问题是自动确定何时这种转移将真正改善分类结果。本项目的目的是发展和评价这种转移模式。该模型的参数将使用贝叶斯估计器从所有可用的训练样本中估计出来，这也需要仔细设计先验概率。贝叶斯方法已经在广泛的任务中证明了自己。主要是因为它们允许在只有少量可用数据的情况下进行稳健估计，其次是因为它们允许对不确定性和未知参数进行系统建模。该估计器将使用基于现代仿真的方法来实现，例如marco - chain - monte - carlo （MCMC）。与现有方法相比，在鲁棒性增强方面，本项目的主要创新可以概括如下：(1)评估训练样本对知识转移的有用性是一个关键问题。现有的方法往往过于乐观（从而导致负迁移），或者需要从目标图像中获得大量的训练样本。我们的转移模型将以新的标准扩展当前的可转移性概念。这将使我们的模型更稳健，更保守，当可用的数据不允许定论。(2)系统将实例-迁移策略和特征-表示-迁移策略结合在一个统一的迁移模型中。(3)现有的转移方法通常被深度整合到一个特定的分类方法中。另一方面，我们的转移模型基本上可以通过一个瘦抽象接口与任何分类方法一起使用。Heipke教授（莱布尼茨<s:1>摄影测量与地理信息研究所Universität汉诺威）的一个研究项目将涵盖迁移学习主题的不同方面。作为研究合作的一部分，我们计划分享数据和结果。

High-resolution aerial- and satellite imagery are the most important sources of spatial information in remote sensing. Classification of these images using supervised learning algorithms is an essential procedure for image processing and interpretation. This requires a training-sample which must be carefully prepared in a laborious process. One paradigm that may help in reducing manual work during this process is called transfer-learning. A robust transfer-model allows us to reuse training-samples from similar data-sets that are related to our target image in either space (in close vicinity) or time (multi-temporal images). The critical issue is to automatically determine when this transfer will actually improve classification results.The aim of this project is the development and evaluation of such a transfer-model. The parameters of this model will be estimated from all available training-samples using a Bayesian estimator, which also requires a carefully devised prior-probability. Bayesian methods have already proved themselves on a wide range of tasks. Mainly, because they allow robust estimation even when only a minimal amount of data is available and secondly, because they allow methodical modelling of uncertainty and unknown parameters. The estimator will be implemented using modern simulation-based methods, such as Marko-Chain-Monte-Carlo (MCMC).Among the increased robustness compared to existing methods, the main innovations of this project can be summarized as follows: (1) Assessing the usefulness for knowledge transfer of a training-sample is a critical issue. Existing methods are often too optimistic (thus causing negative transfer) or require a large training sample from the target image. Our transfer-model will extend current notions of transferability by a new criteria. This will make our model more robust and more conservative when the available data allows no conclusive decision. (2) Our system combines the instance-transfer-strategy and the feature-representation-transfer-strategy in a unified transfer-model. (3) Existing transfer-methods are usually deeply integrated into a specific classification-method. On the other hand our transfer-model can be utilized with basically any classification-method through a thin abstract interface.A research project of Professor Heipke (Institut für Photogrammetrie und GeoInformation, Leibniz Universität Hannover) will cover a different aspect on the topic of transfer-learning. As part of a research cooperation we plan to share data and results.