Improving Automated Annotation of Benthic Survey Images Using Wide-band Fluorescence

Oscar Beijbom, Tali Treibitz, David I. Kline, Gal Eyal, Adi Khen, Benjamin Neal, Yossi Loya, B. Greg Mitchell, David Kriegman

Research output: Contribution to journalReview articlepeer-review

Abstract

Large-scale imaging techniques are used increasingly for ecological surveys. However, manual analysis can be prohibitively expensive, creating a bottleneck between collected images and desired data-products. This bottleneck is particularly severe for benthic surveys, where millions of images are obtained each year. Recent automated annotation methods may provide a solution, but reflectance images do not always contain sufficient information for adequate classification accuracy. In this work, the FluorIS, a low-cost modified consumer camera, was used to capture wide-band wide-field-of-view fluorescence images during a field deployment in Eilat, Israel. The fluorescence images were registered with standard reflectance images, and an automated annotation method based on convolutional neural networks was developed. Our results demonstrate a 22% reduction of classification error-rate when using both images types compared to only using reflectance images. The improvements were large, in particular, for coral reef genera Platygyra, Acropora and Millepora, where classification recall improved by 38%, 33%, and 41%, respectively. We conclude that convolutional neural networks can be used to combine reflectance and fluorescence imagery in order to significantly improve automated annotation accuracy and reduce the manual annotation bottleneck.

Original languageEnglish
Article number23166
JournalScientific Reports
Volume6
DOIs
StatePublished - 29 Mar 2016

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