  Monitoring coral reefs using underwater vehicles increases the range of
marine surveys and availability of historical ecological data by collecting
significant quantities of images. Analysis of this imagery can be automated
using a model trained to perform semantic segmentation, however it is too
costly and time-consuming to densely label images for training supervised
models. In this letter, we leverage photo-quadrat imagery labeled by ecologists
with sparse point labels. We propose a point label aware method for propagating
labels within superpixel regions to obtain augmented ground truth for training
a semantic segmentation model. Our point label aware superpixel method utilizes
the sparse point labels, and clusters pixels using learned features to
accurately generate single-species segments in cluttered, complex coral images.
Our method outperforms prior methods on the UCSD Mosaics dataset by 3.62% for
pixel accuracy and 8.35% for mean IoU for the label propagation task, while
reducing computation time reported by previous approaches by 76%. We train a
DeepLabv3+ architecture and outperform state-of-the-art for semantic
segmentation by 2.91% for pixel accuracy and 9.65% for mean IoU on the UCSD
Mosaics dataset and by 4.19% for pixel accuracy and 14.32% mean IoU for the
Eilat dataset.
