  Robust semantic segmentation of VHR remote sensing images from UAV sensors is
critical for earth observation, land use, land cover or mapping applications.
Several factors such as shadows, weather disruption and camera shakes making
this problem highly challenging, especially only using RGB images. In this
paper, we propose the use of multi-modality data including NIR, RGB and DSM to
increase robustness of segmentation in blurred or partially damaged VHR remote
sensing images. By proposing a cascaded dense encoder-decoder network and the
SELayer based fusion and assembling techniques, the proposed RobustDenseNet
achieves steady performance when the image quality is decreasing, compared with
the state-of-the-art semantic segmentation model.
