Document Type
Article
Publication Date
2020
DOI
10.1007/s41019-020-00126-0
Publication Title
Data Science and Engineering
Volume
5
Issue
2
Pages
111-125
Abstract
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
Repository Citation
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.
https://digitalcommons.odu.edu/ece_fac_pubs/259
ORCID
0000-0002-9399-4264 (Zimmerman)
Included in
Electrical and Computer Engineering Commons, Marine Biology Commons, Oceanography Commons, Plant Biology Commons
Comments
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