Using superpixel- or pixel-based segmentation for efficient green roof digital image classification and rapid estimation of plant species cover

《Using superpixel- or pixel-based segmentation for efficient green roof digital image classification and rapid estimation of plant species cover》

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作者: Didier Técher

来源: URBAN FORESTRY & URBAN GREENING,Vol.76,Issue1,Article 127722

语言: 英文

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作者单位: Short communication"}]},{"#name":"title","$":{"id":"tit0005"},"_":"Using superpixel- or pixel-based segmentation for efficient green roof digital image classification and rapid estimation of plant species cover"}],"floats":[],"footnotes":[],"attachments":[]},"vol-first":"76","vol-iss-suppl-text":"Volume 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摘要: Green roofs are “nature-based solutions” that provide numerous ecosystem services in the context of urban green infrastructures. Plant species diversity and the associated vegetation communities, in strong interactions with green roof substrate, play a central role in the green infrastructure functioning. In order to better understand the influence of vegetation in relation with the co-benefits provided by green roofs as well as to select suitable species for these usually harsh environments, it is essential to be able to achieve accurate and long-term monitoring of plant communities. In this short communication, two free plugins recently developed for the open-source image analysis software Fiji (a distribution of the freely available ImageJ platform, initially dedicated to biological image analysis) were investigated for their capacity to rapidly and efficiently perform supervised machine-learning for the classification of green roof vegetation photographs, with the aim of estimating individual plant species abundance. Two simple methods are thus described using the Trainable Weka Pixel Segmentation (Arganda-Carreras et al., 2017) or the Trainable Superpixel Segmentation (Salinas Colina et al., 2018), which allowed for rapid, efficient and reproducible classification and estimation of multispecies colonized green roof regardless the color or shape similarities among species or ground cover materials. Finally, recommendations are made for the use of the Trainable Superpixel Segmentation which is particularly convenient for quick and efficient green roof image analysis.