Year: 2016
Type: dissertation
Abstract: Many tasks in the field of computer vision rely on an underlying change detection algorithm in images or video sequences. Although much research has focused on change detection in consumer images, there is little work related to change detection on aerial imagery, where individual images are recorded from aerial platforms over time.\nThis thesis presents two deep learning approaches for detection in aerial images. Both systems leverage Spatial Transformer Networks (STN) that identify the coordinate transformation for their localization capabilities. The first approach is based on a semisupervised approach which learns to locate changes within a difference image. The second is a fully-supervised approach which learns to locate and discriminate relevant targets. The supervised approach is shown to locate nearly 78% of positive samples with an Intersection Over Union (IOU) criterion of over 0.5, and nearly 94% of positive samples with an IOU over 0.3.
Author: Daniel Chianucci
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2587568685