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Título
Semantic segmentation of major macroalgae in coastal environments using high-resolution ground imagery and deep learning
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
International Journal of Remote Sensing
Número de la revista
5
Cita Bibliográfica
Jesús Balado, Celia Olabarria, Joaquín Martínez-Sánchez, José R. Rodríguez-Pérez & Arias Pedro (2021) Semantic segmentation of major macroalgae in coastal environments using high-resolution ground imagery and deep learning, International Journal of Remote Sensing, 42:5, 1785-1800 is available at https://doi.org/10.1080/01431161.2020.1842543
Editorial
Taylor & Francis
Fecha
2021-01-01
ISSN
1366-5901
Resumen
Macroalgae are a fundamental component of coastal ecosystems and play a key role in shaping community structure and functioning. Macroalgae are currently threatened by diverse stressors, particularly climate change and invasive species, but they do not all respond in the same way to the stressors. Effective methods of collecting qualitative and quantitative information are essential to enable better, more efficient management of macroalgae. Acquisition of high-resolution images, in which macroalgae can be distinguished on the basis of their texture and colour, and the automated processing of these images are thus essential. Although ground images are useful, labelling is tedious. This study focuses on the semantic segmentation of five macroalgal species in high-resolution ground images taken in 0.5 x 0.5 m quadrats placed along an intertidal rocky shore at low tide. The target species, Bifurcaria bifurcata, Cystoseira tamariscifolia, Sargassum muticum, Sacchoriza polyschides and Codium spp., which predominate on intertidal shores, belong to different morpho-functional groups. The study explains how to convert vector-labelled data to raster-labelled data for adaptation to convolutional neural network (CNN) input. Three CNNs (MobileNetV2, Resnet18, Xception) were compared, and ResNet18 yielded the highest accuracy (91.9%). The macroalgae were correctly segmented, and the main confusion occurred at the borders between different macroalgal species, a problem derived from labelling errors. In addition, the interior and exterior of the quadrats were correctly delimited by the CNNs. The results were obtained from only one hundred labelled images and can be performed on personal computers, without the need to resort to external servers. The proposed method helps automation of the labelling process.
Materia
Palabras clave
Peer review
SI
ID proyecto
- ED481B-2019-061
- ED431C 2016-038
- RTI2018-095893-B-C21
URI
DOI
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Versión CC 4.0 del artículo https://doi.org/10.1080/01431161.2020.1842543