Unsupervised feature learning for autonomous rock image classification. (September 2017)

• Record Type: Journal Article
• Authors: Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond
• Is Part Of: Computers & geosciences. Volume 106(2017)
• Page Start: 10
• Page End: 17
• Publication Date: 2017-09
• Subjects: Unsupervised feature learning -- Self-taught learning -- Autonomous rock classification; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2017.05.010
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images. Abstract : Highlights: We propose to learn feature representation with K-means for rock image classification. We show the unsupervised feature learning is flexible and can outperform manual features on rock image classification. We prove self-taught learning is feasible to learn the feature representation from unlabelled rock images.