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dc.contributor.advisorRozario, Papia F.
dc.contributor.advisorGomes, Rahul
dc.contributor.authorMohan, Pavithra Devy
dc.contributor.authorDeWitte, Matthew
dc.descriptionColor poster with text, images, charts, photographs, and graphs.en_US
dc.description.abstractAs multispectral image resolution has increased, generating accurate segmentation of these images can pose a significant challenge. Furthermore, creating accurate labeled data requires hours of manual segmentation. One solution to this problem is the application of deep learning algorithms which can learn non-linear trends in the data without significant preprocessing and be used for transfer learning. In this research, we demonstrate transfer learning on how a model trained on one dataset can be used to segment a different dataset. Using the U-Net deep learning algorithm, we first train our model on a dataset with class labels. We then use the trained model to extend a custom U-Net structure to transfer semantic knowledge from the previous training and adapt to the unknown images. Preliminary results indicate that there is a potential to achieve higher accuracy by using optimized loss functions suited for unsupervised learning along with pre-trained weights from the trained U-Net model.en_US
dc.description.sponsorshipUniversity of Wisconsin--Eau Claire Office of Research and Sponsored Programsen_US
dc.relation.ispartofseriesUSGZE AS589;
dc.subjectMachine learningen_US
dc.subjectRemote sensingen_US
dc.subjectImage processingen_US
dc.subjectDepartment of Geography and Anthropologyen_US
dc.subjectDepartment of Computer Scienceen_US
dc.titleUsing Deep Transfer Learning for Unsupervised Image Segmentation in Remote Sensingen_US

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    Posters of collaborative student/faculty research presented at CERCA

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