Data from: Improving automated annotation of benthic survey images using wide-band fluorescence

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.大规模成像技术在生态调查中的应用日益广泛。然而人工分析的成本往往高得难以承受，在已采集的图像与预期的数据产品之间形成了一道瓶颈。这一瓶颈在底栖调查（benthic surveys）中尤为突出——这类调查每年都会产生数百万幅图像。近年来涌现的自动化标注方法或可解决这一难题，但反射率图像（reflectance images）往往无法提供足够的信息以保证足够的分类精度。本研究采用一款经过改造的低成本民用相机FluorIS，在以色列埃拉特（Eilat）的野外部署场景中采集了宽波段宽视场荧光图像。随后将荧光图像与标准反射率图像进行配准，并开发了一种基于卷积神经网络（convolutional neural networks）的自动化标注方法。实验结果表明，与仅使用反射率图像相比，同时使用两类图像可使分类错误率降低22%。这一提升幅度十分显著，尤其是在扁脑珊瑚属（Platygyra）、鹿角珊瑚属（Acropora）和千孔珊瑚属（Millepora）中，其分类召回率分别提升了38%、33%与41%。综上，卷积神经网络可用于融合反射率与荧光图像，从而显著提升自动化标注精度，缓解人工标注的瓶颈问题。