Pubblicazione scientifica

Seagrass Propeller Scar Detection using Deep Convolutional Neural Network

FILONE TECNOLOGICO	TEMA
produzione e cattura

RIASSUNTO

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.

DATA

Data di pubblicazione: 01/11/2018

AUTORI

MD RESHAD UL HOQUE
KAZI AMINUL ISLAM
DANIEL PEREZ
VICTORIA HILL
BLAKE SCHAEFFER
RICHARD ZIMMERMAN
JIANG LI

ENTE DI AFFERENZA

DEPT ELEC & COMPUTER ENG OLD DOMINION UNIV NORFOLK VIRGINIA
DEPT MODELING OLD DOMINION UNIV NORFOLK VIRGINIA
DEPT OCEAN OLD DOMINION UNIV NORFOLK VA 23529 USA
OFFICE OF RSCH & DEV US ENVIRONMENTAL PROTECTION AGENCY

RIVISTA

2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (Page(s): 659-665)

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