  Deep convolutional neural networks (DCNNs) have been used to achieve
state-of-the-art performance on many computer vision tasks (e.g., object
recognition, object detection, semantic segmentation) thanks to a large
repository of annotated image data. Large labeled datasets for other sensor
modalities, e.g., multispectral imagery (MSI), are not available due to the
large cost and manpower required. In this paper, we adapt state-of-the-art DCNN
frameworks in computer vision for semantic segmentation for MSI imagery. To
overcome label scarcity for MSI data, we substitute real MSI for generated
synthetic MSI in order to initialize a DCNN framework. We evaluate our network
initialization scheme on the new RIT-18 dataset that we present in this paper.
This dataset contains very-high resolution MSI collected by an unmanned
aircraft system. The models initialized with synthetic imagery were less prone
to over-fitting and provide a state-of-the-art baseline for future work.
