Leveraging Google Earth Engine and Semi-Supervised Generative Adversarial Networks to Assess Initial Burn Severity in Forest

Abstract Mapping and monitoring initial burn severity is a critical aspect of forest management and landscape restoration. Recently, supervised learning has achieved great success in evaluating the post-fire forest condition, but plenty of labeled samples are needed to carry out training for supervised learning. However, due to the limitation of accessible labeled data, it is often arduous to put supervised learning into practice in the remote sensing field. In this paper, a novel semi-unsupervised image classification framework by using generative adversarial networks under the Google Earth Engine (GEE) platform is proposed to undertake the burn severity assessment with limited samples. The generative model can produce additional adversarial samples that look like the real samples; therefore, the discriminative model gains better classification capabilities in burn severity assessment with the extra training data provided by the generator. The detailed evaluation and comparative experiments are done in the context of post-fire assessment on eleven forest fires in northern California in the United States. The experimental results demonstrate that our approach significantly outperforms the two other most well-known methods.