Simultaneous contextual classification of multitemporal and multiscale remote sensing imagery based on existing GIS data for training

基于现有GIS数据对多时相、多尺度遥感影像进行同步上下文分类进行训练

• 批准号: 290281376
• 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: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/290281376?language=en
• 关键词: Simultaneous; contextual; classification; multitemporal; multiscale

It is the goal of the proposed project to develop a novel methodology for the supervised context-based classification of multitemporal and multiscale remote sensing imagery without any manually labelled training data. The main scientific contribution is the development of new training methods that are tolerant to label noise, i.e., to a considerable amount of training samples with erroneous class labels. Using these methods it should become possible to use existing land cover (LC) data to derive class labels to be used for training for all pixels of an image to be classified. Rather than using sparse hand-labelled training data, we propose using an abundance of training data generated automatically, along with methods that can deal with the inevitable errors in these data. The mathematical framework for the proposed methodology is given by Conditional Random Fields (CRF). We will build a CRF that can classify data from multiple epochs and having different geometrical resolutions simultaneously, considering the fact that LC data at multiple resolutions will be characterised by different class structures. We rely on the existence of both, global, regional and local LC data sets to derive training data. We will develop new probabilistic approaches for considering label noise in training in order to obtain not only the parameters of the classifiers linking the unknown class labels of the CRF with the data, but also the parameters linking the images at different epochs with each other. As an important contribution we will consider the fact that errors in LC data are spatially correlated. The suggested project constitutes the first application of the principles of label-noise tolerant training procedures in the context of graph-based image classification, and one of the most general techniques for considering interactions between objects modelled at different semantic levels of detail. As a consequence, it should become possible to cut the costs for the update of global or regional and local LC data sets, e.g. by using cheap imagery of low resolution to get hints for changes in the high-resolution data. The new methodology is evaluated on real data with a reference that was generated manually. In the frame of an existing Memorandum of Understanding with the National Geomatics Center of China (NGCC) we will investigate the methodology in different test sites in Germany and China. We will use the global land cover data set GLC30 with 30 m geometrical resolution, developed by NGCC and available free of charge, as the coarse-resolution data set in our test cases. The high-resolution data sets we will use are those from the German Survey Authorities and NGCC, respectively.

拟议项目的目标是开发一种新的方法，在没有任何人工标记的训练数据的情况下，对多时相和多尺度遥感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了能够容忍标签噪声的新训练方法，即，大量带有错误类别标签的训练样本。使用这些方法，它应该成为可能使用现有的土地覆盖（LC）数据，以获得类标签用于训练的图像的所有像素进行分类。我们建议使用大量自动生成的训练数据，而不是使用稀疏的手工标记的训练数据，沿着可以处理这些数据中不可避免的错误的方法。所提出的方法的数学框架是由条件随机场（CRF）。我们将建立一个CRF，它可以对来自多个时期的数据进行分类，同时具有不同的几何分辨率，考虑到多个分辨率的LC数据将具有不同的类结构。我们依赖于全球，区域和本地LC数据集的存在来获得训练数据。我们将开发新的概率方法，在训练中考虑标签噪声，以便不仅获得将CRF的未知类标签与数据连接起来的分类器的参数，而且还获得将不同时期的图像相互连接起来的参数。作为一个重要的贡献，我们将考虑这样一个事实，即在LC数据的误差是空间相关的。建议的项目构成了标签噪声容忍训练程序的原则在基于图形的图像分类的上下文中的第一个应用程序，以及考虑在不同语义层次的细节建模的对象之间的相互作用的最一般的技术之一。因此，应该有可能削减更新全球或区域和地方LC数据集的费用，例如，通过使用低分辨率的廉价图像来获得高分辨率数据变化的提示。新的方法进行了评价的真实的数据与手动生成的参考。在与中国国家地理信息中心（NGCC）的现有谅解备忘录的框架内，我们将在德国和中国的不同测试地点调查该方法。我们将使用全球土地覆盖数据集GLC 30与30米的几何分辨率，由NGCC开发和免费提供，作为我们的测试用例中的粗分辨率数据集。我们将使用的高分辨率数据集分别来自德国测量局和NGCC。

项目成果

Classification Under Label Noise Based on Outdated Maps

基于过时地图的标签噪声下的分类

• DOI: 10.5194/isprs-annals-iv-1-w1-215-2017
• 发表时间: 2017
• 期刊: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• 作者: Rottensteiner;Heipke
• 通讯作者: Heipke

Multitemporal Classification Under Label Noise Based on Outdated Maps

基于过时地图的标签噪声下的多时相分类

• DOI: 10.14358/pers.84.5.263
• 发表时间: 2018
• 期刊: Photogrammetric Engineering and Remote Sensing
• 影响因子: 1.3
• 作者: Rottensteiner;Alobeid;Heipke
• 通讯作者: Heipke

AUTOMATIC CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGERY – ACASE STUDY FOR URBAN AREAS IN THE KINGDOM OF SAUDI ARABIA

高分辨率卫星图像的自动分类——沙特阿拉伯王国城市地区的案例研究

• DOI: 10.5194/isprs-archives-xlii-1-w1-11-2017
• 发表时间: 2017
• 期刊: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• 作者: Alrajhi;Alobeid;Heipke C.
• 通讯作者: Heipke C.

A label noise tolerant random forest for the classification of remote sensing data based on outdated maps for training

• DOI: 10.1016/j.cviu.2019.07.002
• 发表时间: 2019-11
• 期刊: Comput. Vis. Image Underst.
• 作者: Alina E. Maas;F. Rottensteiner;C. Heipke

Label Noise Robust Classification Of Hyperspectral Data

• DOI: 10.1109/whispers.2018.8747035
• 发表时间: 2018-09
• 期刊: 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
• 作者: Alina E. Maas;Behnood Rasti;M. Ulfarsson