Can unsupervised machine learning help us understand the data collected in the ocean?

Abstract

Since the ocean is not a convenient place to conduct research, data are often collected from offshore buoys. However, the data collected from the buoys usually do not have labels (e.g., source of the sound) attached to them. Manually labeling the data is time-consuming, but labeled data are necessary to use powerful supervised machine learning methods. We propose that unsupervised learning can be used to assign preliminary labels to data samples. To show the utility of this approach, we took an echosounder to Utah Lake and collected sonar images, some of which showed evidence of fish. We used Python to crop the images, similar to how regions of interest are identified in image processing. These cropped images are used in an unsupervised k-means clustering algorithm. The images are clustered based on visual features. An analysis of the resulting clusters and the feasibility of using unsupervised learning to provide preliminary labels to data is discussed.