Cross-Domain Identification of Road Networks Using Domain-Adapted Convolutional Neural Networks
Abstract
Convolutional neural networks (CNNs) are a powerful tool for identification of patterns and objects within imagery or video. Training CNNs that can generalize well to their intended target dataset can require large amounts of labeled source data. The characteristics and distribution of this source (training) data must be representative of the target dataset for it to perform well. Labeled source data that fits this requirement is not always readily available. Research published by Ganin et al., in a 2016 paper titled "Domain-Adversarial Training of Neural Networks," demonstrates that CNNs trained on a labeled source dataset can be adapted to generalize well to a target dataset through a process called domain adaption. In their research, they show that domain-adversarial neural networks (DANNs) improve performance on their target dataset relative to non-adapted CNNs. The purpose of this research is to explore the ability of DANNs to improve unmanned aerial vehicle (UAV) onboard classification of objects by adapting a CNN trained on satellite imagery to UAV aerial imagery. We show that DANNs do improve performance for this use case using several DANN architectures and datasets. This furthers other Naval Postgraduate School research efforts into autonomous UAV navigation and identification of targets of interest.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 2020
- Accession Number
- AD1114280
Entities
People
- Teal A. Peterson
Organizations
- Naval Postgraduate School