Sensors, Vol. 20, Pages 447: Automatic Hierarchical Classification of Kelps Using Deep Residual Features

Sensors, Vol. 20, Pages 447: Automatic Hierarchical Classification of Kelps Using Deep Residual Features Sensors doi: 10.3390/s20020447 Authors: Ammar Mahmood Ana Giraldo Ospina Mohammed Bennamoun Senjian An Ferdous Sohel Farid Boussaid Renae Hovey Robert B. Fisher Gary A. Kendrick 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, such as 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 class...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research