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.

深度卷积神经网络（DCNN）已被用于实现许多计算机视觉任务（例如，对象识别，对象检测，语义分割）的最新性能，这要归功于带有批注图像数据的大型存储库。由于所需的大量成本和人力，因此无法获得用于其他传感器模式（例如多光谱图像（MSI））的大标签数据集。在本文中，我们将计算机视觉中最先进的DCNN框架应用于MSI图像的语义分割。为了克服MSI数据的标签稀缺性，我们将实际MSI替换为生成的合成MSI，以初始化DCNN框架。我们在本文介绍的新RIT-18数据集上评估网络初始化方案。该数据集包含由无人飞机系统收集的非常高分辨率的MSI。