Onboard detection of natural sulfur on a glacier via a SVM and Hyperion data

Onboard classification of remote sensing data is of general interest given that it can be used as a trigger to initiate alarms, data download, additional higher-resolution scans, or more frequent scans of an area without ground interaction. In our case, we study the sulfur-rich Borup-Fiord glacial springs in Canada utilizing the Hyperion instrument aboard the EO-1 spacecraft. This system consists of naturally occurring sulfur-rich springs emerging from glacial ice, which are a known environment for microbial life. The biological activity of the spring is associated with sulfur compounds that can be detected remotely via spectral analysis. This system may offer an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Unfortunately, spacecraft processing power and memory is severely limited which places strong constraints on the algorithms available. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of sulfur compounds associated with the activity of microbial life. However, those results were limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. Of key interest is our assessment of the classifier's behavior on non-sulfur-bearing imagery far from the training region. Selection of the most relevant spectral bands and parameters for the SVM are also explored.