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Application of Deep Learning for Environmental Information Extraction from Remote Sensing Imagery

thesis
posted on 19.02.2019, 12:48 by Guang Xu
Convolutional Neural Network (CNN) is most promising for remote sensing imagery classification. However, a CNN model usually contains multiple convolutional and pooling layers for image feature extraction, which consumes lots of computational resources and makes it difficult for environmental scientists to utilize it. This study proposed the innovative Hierarchical Active Learning framework to deal with the situation when working with large satellite imagery with no need for a large number of labelled samples and expensive computational resources. The results of the study have demonstrated the value of multi-sourced data, which has opened up a new area in citizen science.

History

Campus location

Australia

Principal supervisor

Xuan Zhu

Additional supervisor 1

Nigel Tapper

Year of Award

2019

Department, School or Centre

Earth, Atmosphere and Environment

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Science