Style transfer-based domain adaptation for vegetation segmentation with optical imagery
Style transfer methods are an important task for domain adaptation of optical imagery to improve the performance of deep learning models when using different sensor systems. For the transformation between datasets, cycle-consistent adversarial networks achieve good results. However, during the style transfer process, characteristic spectral information that is essential for the analysis of vegetation could get lost. This issue is especially important since optical airborne- and spaceborne-based sensors are frequently used to investigate vegetation ground coverage and its condition. In this paper, we present a cycle-consistent adversarial domain adaptation method with four input channels for the segmentation of vegetation areas using index-based metrics. We show that our method preserves the specific ratio between the near-IR and RGB bands and improves the segmentation network performance for the target domain.