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Deep transfer learning and its applications in remote sensing and computer vision

University of British Columbia

Several machine learning tasks rely on the availability of large amounts of data. To obtain robust machine learning systems, the employment of annotated data samples is crucial. For computer vision tasks, the shortage of annotated training data has been a hindrance. To address this problem, one of the most popular approaches is deep transfer learning (DTL). DTL methods transfer Information from annotated large datasets to a scarce number of un-annotated datasets. This transfer employs deep learning to find the features of all annotated and un-annotated image datasets. The labels of un-annotated datasets are determined by finding the label of the annotated ones that share similar features. This thesis proposes different deep transfer learning models for problems with three types of image data: aerial images, satellite images, and ground-view images. Based on these image datasets, our transfer learning tasks include the transfer learning between the different types of regular images, between the different types of remote sensing images, and between the remote sensing and regular images. The underlying relationships are obtained by setting up a correlation between the deep transfer learning models corresponding to the different types of images. The proposed models address three research tasks. The first task addresses the "what to transfer” problem, i.e., finding the appropriate content for transfer. For this task, we propose an active learning incorporated deep transfer learning model which explores the relationships among different remote sensing images; The second task studies the "where to transfer” problem, and finds the correlation between the deep learning networks of the annotated and the un-annotated images. For this task, we considered regular images. The third task investigates the "how to transfer” problem for three types of images (aerial, satellite and ground-view), and involves finding the image relationships and the best deep learning neural network models for knowledge transfer. Several models, including the Dual Space structure preserving Transfer Learning model, the Xnet, and the Dual Adversarial Network (DuAN), are proposed.

Text

2020-05-04

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/74272

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/