Edinburgh Research Explorer

Automatic Hierarchical Classification of Kelps utilizing Deep Residual Feature

Research output: Contribution to journalArticle

  • Ammar Mahmood
  • Ana Giraldo Ospina
  • Mohammed Bennamoun
  • Senjian An
  • Ferdous Sohel
  • Farid Boussaid
  • Renae Hovey
  • Bob Fisher
  • Gary Kendrick

Related Edinburgh Organisations

Open Access permissions

Open

Documents

  • Download as Adobe PDF

    Accepted author manuscript, 1.77 MB, PDF document

    Licence: Creative Commons: Attribution (CC-BY)

  • Download as Adobe PDF

    Final published version, 1.8 MB, PDF document

    Licence: Creative Commons: Attribution (CC-BY)

https://www.mdpi.com/1424-8220/20/2/447
Original languageEnglish
Article number447
Number of pages20
JournalSensors
Volume20
Issue number2
DOIs
Publication statusPublished - 13 Jan 2020

Abstract

Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas. Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. With this limitation in mind, a large effort has been made globally to introduce automation and machine learning algorithms to accelerate both classification and assessment of marine benthic biota. One major issue lies with organisms that move with swell and currents, like kelps. This paper presents an automatic hierarchical classification method (local binary classification as opposed to the conventional flat classification) to classify kelps in images collected by autonomous underwater vehicles. The proposed kelp classification approach exploits learned feature representations extracted from deep residual networks. We show that these generic features outperform the traditional off-the-shelf CNN features and the conventional hand-crafted features. Experiments also demonstrate that the hierarchical classification method outperforms the traditional parallel multi-class classifications by a significant margin (90.0% vs 57.6% and 77.2% vs 59.0%) on Benthoz15 and Rottnest datasets respectively. Furthermore, we compare different hierarchical classification approaches and experimentally show that the sibling hierarchical training approach outperforms the inclusive hierarchical approach by a significant margin. We also report an application of our proposed method to study the change in kelp cover over time for annually repeated AUV surveys.

    Research areas

  • deep learning, hierarchical classification, kelp cover, kelps, manual annotation, benthic marine population analysis

Download statistics

No data available

ID: 128882514