Electronic Thesis and Dissertation Repository


Master of Engineering Science


Electrical and Computer Engineering

Collaborative Specialization

Planetary Science and Exploration


McIsaac, Ken

2nd Supervisor

Osinski, Gordon



Autonomous image recognition has numerous potential applications in the field of planetary science and geology. For instance, having the ability to classify images of rocks would allow geologists to have immediate feedback without having to bring back samples to the laboratory. Also, planetary rovers could classify rocks in remote places and even in other planets without needing human intervention. In 2017, Shu et. al. used a Support Vector Machine (SVM) classification algorithm to classify 9 different types of rock images using a with the image features extracted autonomously. Through this method, they achieved a test accuracy of 96.71%. Within the last few years, Convolutional Neural Networks (CNNs) have been shown to be perform better than other algorithms in classifying images of everyday objects. In light of this development, this thesis demonstrates the use of CNNs to classify the same set of rock images. With the addition of dataset augmentation, a 3-layer CNN is shown to have a significant improvement over Shu et. al.'s results, achieving an average accuracy of 99.60% across 10 trials on the test set. Multiple CNN operations with similar output shapes have been designed and appended to an existing architecture to expand hyperparameter considerations. These Combinational Fully Connected Neural Networks achieves an accuracy of 99.36% on the test set. The resulting models are also shown to be lightweight enough that they can be deployed on a mobile device. To tackle a more interesting and practical problem, CNNs have also been designed to classify natural scene images of rocks, an inherently more complex dataset. The task has been simplified into a binary classification problem where the images are classified into breccia and non-breccia. This thesis shows that a Combinational Fully Connected Neural Network achieves an accuracy of 93.50%, better than a 5-layer CNN, which achieves 89.43%.