Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning
This work addresses the challenge of limited labeled data for multispectral imagery in remote sensing, providing a baseline for future research in this domain-specific area.
The paper tackles the problem of semantic segmentation for multispectral remote sensing imagery by adapting deep convolutional neural networks and using synthetic data to overcome label scarcity, achieving state-of-the-art performance with models less prone to over-fitting on the new RIT-18 dataset.
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.