Comparison of machine learning methods for mapping sea farms with high spatial resolution imagery

In general, sea farms are classified into two types depending on their locations, seawater depths, and cultivation species: underwater and above water farms. This study compared the machine learning techniques (random forest, a supervised learning technique, and K-means, an unsupervised learning technique) for mapping both underwater and above water farms using the high-resolution satellite image acquired in the sea farming areas of the South Sea of South Korea through the following steps. First, each machine learning algorithm was separately used in the given high-resolution satellite image to generate a sea surface map. Then underwater and above water farms were detected from each sea surface map. Finally, the accuracy of both the underwater and above water farms detected from the different sea surface maps was assessed. The experimental results led to the following conclusions. First, random forest, a supervised learning technique, has better performance than K-means, an unsupervised learning technique, for detecting both underwater and above water farms from the given high-resolution satellite image. Second, only a few misclassification errors occurred in both the underwater and above water farms detected by the random forest algorithm in the given image, with the underwater farms misclassified as having above water features, or vice versa.