Paper Info
Title
Seagrass Propeller Scar Detection using Deep Convolutional Neural Network
Abstract
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 1000×800 in testing phase.
Year
DOI
Venue
2018
10.1109/UEMCON.2018.8796636
2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
Keywords
Field
DocType
Seagrass,Pan-sharpening,Convolutional Neural Network
Intrusion,Seagrass,Pattern recognition,Propeller,Computer science,Convolutional neural network,Neural coding,Multispectral image,Human–computer interaction,Artificial intelligence,Deep learning
Conference
ISBN
Citations 
PageRank 
978-1-5386-7694-3
0
0.34
References 
Authors
7
7
Name
Order
Citations
PageRank
Md Reshad Ul Hoque100.34
Kazi Aminul Islam200.34
Daniel Pérez392.63
Victoria Hill422.08
blake a schaeffer5103.79
Richard Zimmerman622.08
jiang li7239.88