SeaGrassDetect: A novel method for detection of seagrass from unlabelled under water videos

Abstract Benthic vegetation is arguably one of the most important indicators of the state of marine environment. Assessment of the status of eelgrass (Zostera marina) is commonly done using various remote sensing methods such as aerial photography or satellite images. These methods often fail to capture the true scenario beneath the surface of the water if the water is turbid or the satellite image is masked by cloud cover which makes it impossible to see beneath them. As a second line of defense, researchers have used under water videos (obtained either with scuba,snorkel or other visual observations) to assess the ground truth. Lots of man-hours are spent browsing through many hours of video data manually by an expert and assessing the status (presence/absence) which is a very common practice. Here we propose two methods for detection of eelgrass (presence/absence) from under water videos obtained from Roskilde Fjord in Denmark. We extend these methods to show that it can be used as a proxy to estimate coverage of eelgrass in a given area which match well with an expert's estimation. The benefit of using this method is that it is objective, less biased, cost efficient, robust to noisy environment, does not require pixel-level annotated ground truth images and can be used on existing video transects. This method can also detect rare errors from domain expert's visual estimation.