  Marine scientists use remote underwater video recording to survey fish
species in their natural habitats. This helps them understand and predict how
fish respond to climate change, habitat degradation, and fishing pressure. This
information is essential for developing sustainable fisheries for human
consumption, and for preserving the environment. However, the enormous volume
of collected videos makes extracting useful information a daunting and
time-consuming task for a human. A promising method to address this problem is
the cutting-edge Deep Learning (DL) technology.DL can help marine scientists
parse large volumes of video promptly and efficiently, unlocking niche
information that cannot be obtained using conventional manual monitoring
methods. In this paper, we provide an overview of the key concepts of DL, while
presenting a survey of literature on fish habitat monitoring with a focus on
underwater fish classification. We also discuss the main challenges faced when
developing DL for underwater image processing and propose approaches to address
them. Finally, we provide insights into the marine habitat monitoring research
domain and shed light on what the future of DL for underwater image processing
may hold. This paper aims to inform a wide range of readers from marine
scientists who would like to apply DL in their research to computer scientists
who would like to survey state-of-the-art DL-based underwater fish habitat
monitoring literature.
