Deep Fusion of Imaging Modalities for Semantic Segmentation of Satellite Imagery

In this report I summarize my master’s thesis work, in which I have investigated different approaches for fusing imaging modalities for semantic segmentation with deep convolutional networks. State-of-the-art methods for semantic segmentation of RGB-images use pre-trained models, which are fine-tuned to learn task-specific deep features. However, the use of pre-trained model weights constrains the model input to images with three channels (e.g. RGB-images). In some applications, e.g. classification of satellite imagery, there are other imaging modalities that can complement the information from the RGB modality and, thus, improve the performance of the classification. In this thesis, semantic segmentation methods designed for RGB images are extended to handle multiple imaging modalities, without compromising on the benefits, that pre-training on RGB datasets offers. In the experiments of this thesis, RGB images from satellites have been fused with normalised difference vegetation index (NDVI) and a digital surface model (DSM). The evaluation shows that the modality fusion can significantly improve the performance of semantic segmentation networks in comparison with a corresponding network with only RGB input. However, the different investigated approaches to fuse the modalities proved to achieve similar performance. The conclusion of the experiments is, that the fusion of imaging modalities is necessary, but the method of fusion has shown to be of less importance