Urban Land Cover Classification with Missing Data Using Deep Convolutional Neural Networks
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Automatic urban land cover classification is a classical problem in remote sensing and good urban land cover maps build the foundation for many tasks, such as e.g. environmental monitoring. It is a particularly challenging problem, as classes generally have high inter-class and low intra-class variance. A common technique to improve urban land cover classification performance in remote sensing is the fusing of data from different sensors with different data modalities. However, all modalities are rarely available for all test data, and this missing data problem poses severe challenges for multi-modal learning. Inspired by recent successes in deep learning, we propose as a remedy a convolutional neural network (CNN) architecture for urban remote sensing image segmentation trained on data modalities which are not all available at test time. We train our architecture with a cost function particularly suited for imbalanced classes, as this is a frequent problem in remote sensing, especially in urban areas. We demonstrate the method using two benchmark datasets, both consisting of optical and digital surface model (DSM) images. We simulate missing data, by assuming that the DSM images are missing during testing and show that our method outperforms both CNNs trained on optical images as well as an ensemble of two CNNs trained only on optical images. We further evaluate the potential of our method to handle situations where only some DSM images are missing during training and show that we can clearly exploit training time information of the missing modality during testing.
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