A Deep Learning Approach to the Detection of Gossans in the Canadian Arctic

• Published in: Remote Sensing
• Main Authors: Étienne Clabaut, Myriam Lemelin, Mickaël Germain, Marie-Claude Williamson, Éloïse Brassard
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2020
• Subjects: gossan; deep learning; convolutional neural network; geo big data; multispectral; Landsat; Science; Q; Arctic
• Online Access: https://doi.org/10.3390/rs12193123; https://doaj.org/article/1fad45b1919546f788b748e966a611c1

Gossans are surficial deposits that form in host bedrock by the alteration of sulphides by acidic and oxidizing fluids. These deposits are typically a few meters to kilometers in size and they constitute important vectors to buried ore deposits. Hundreds of gossans have been mapped by field geologists in sparsely vegetated areas of the Canadian Arctic. However, due to Canada’s vast northern landmass, it is highly probable that many existing occurrences have been missed. In contrast, a variety of remote sensing data has been acquired in recent years, allowing for a broader survey of gossans from orbit. These include band ratioing or methods based on principal component analysis. Spectrally, the 809 gossans used in this study show no significant difference from randomly placed points on the Landsat 8 imageries. To overcome this major issue, we propose a deep learning method based on convolutional neural networks and relying on geo big data (Landsat-8, Arctic digital elevation model lithological maps) that can be used for the detection of gossans. Its application in different regions in the Canadian Arctic shows great promise, with precisions reaching 77%. This first order approach could provide a useful precursor tool to identify gossans prior to more detailed surveys using hyperspectral imaging.