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

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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.