Seagrass Propeller Scar Detection using Deep Convolutional Neural Network

Seagrass habitats are becoming extremely vulnerable due to human intrusion to seagrass meadows, which results in unbalanced marine ecosystems and extinction of marine animals. Traditionally, manual scarring has been used to identify and quantify seagrass propeller scars. However, this method requires site visitation and it is cost ineffective. In this paper, we propose deep learning method to automatically detect propeller seagrass scars in multispectral satellite images. Our proposed algorithm is more computationally efficient than our previous sparse coding detection model and can accurately detect seagrass scars. Additionally, we explored two pan-sharpening methods for obtaining high-resolution multispectral satellite images for scar detection. We evaluated our methods on four multispectral images collected in Florida and experimental results show that the proposed deep learning model combined with the Gram-Smith (GS) pan-sharpening approach achieved the best sensitivities in seagrass scar detection and this combination is also the most computational efficient method, requiring only 7 minutes for a testing image with a size of 1000x800 in testing phase.