Data Science and Engineering
Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from both data domains. Second, we used a few labelled samples from the target domain to align class specific data distributions between the two domains, based on the contrastive semantic alignment loss. We achieved the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods.
Original Publication Citation
Islam, K. A., Hill, V., Schaeffer, B., Zimmerman, R., & Li, J. (2020). Semi-supervised adversarial domain adaptation for seagrass detection using multispectral images in coastal areas. Data Science and Engineering, 5(2), 111-125. https://doi.org/10.1007/s41019-020-00126-0
Islam, Kazi Aminul; Hill, Victoria; Schaeffer, Blake; Zimmerman, Richard; and Li, Jiang, "Semi-Supervised Adversarial Domain Adaptation for Seagrass Detection Using Multispectral Images in Coastal Areas" (2020). Electrical & Computer Engineering Faculty Publications. 259.