|
Segmentation of individual seagrass images is of importance to biologists who are investigating individual seagrass blade cover to correlate the surface cover information to benthic environmental factors. Seagrasses may be covered with epiphytes like crustose and filamentous algae and tubeworms, all bioindicators of nutrient and turbidity conditions of the seagrass environment. Classical image processing techniques to segment seagrasses have been successful; however, such techniques are relatively time consuming. We introduce deep learning as a computationally efficient approach to perform semantic segmentation in multiple seagrass images to determine each blade’s percent cover and surface composition. Pre-trained ResNet-18 and ResNet-50 convolutional neural networks have been adapted using transfer learning to classify seagrass blade surface composition. Seagrass surface semantic segmentation and mapping is achieved for five classes including the bare seagrass blade (no cover), general epiphyte, tubeworm, filamentous algae, and background. We present the application of deep learning in two convolutional neural networks to achieve semantic segmentation of seagrass blades as a fast tool for seagrass surface classification. Classification accuracy and computational performance of the two deep CNN are presented.
|
