<div>Convolutional neural networks lie at the heart of nearly every object recognition system today. While their performance continues to improve through new architectures and techniques, some of their deciencies have not been fully addressed to date. Two of these deciencies are their inability to distinguish the spatial relationships between features taken from the data, as well as their need for a vast amount of training data. Capsule networks, a new type of convolutional neural network, were designed specically to address these two issues. In this work, several capsule network architectures are utilized to classify objects taken from overhead satellite imagery. These architectures are trained and tested on small datasets that were constructed from the xView dataset, a comprehensive collection of satellite images originally compiled for the task of object detection. Since the objects in overhead satellite imagery are taken from the same viewpoint, the transformations exhibited within each individual object class consist primarily of rotations and translations. These spatial relationships are exploited by capsule networks. As a result it is shown that capsule networks achieve considerably higher accuracy when classifying images from these constructed datasets than a traditional convolutional neural network of approximately the same complexity.</div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8035424 |
| Date | 11 June 2019 |
| Creators | Darren Rodriguez (6630416) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Classifying_Objects_from_Overhead_Satellite_Imagery_Using_Capsules/8035424 |
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