RT info:eu-repo/semantics/article
T1 Semantic segmentation of major macroalgae in coastal environments using high-resolution ground imagery and deep learning
A1 Balado, Jesús
A1 Rodríguez Pérez, José Ramón
A1 Arias, Pedro
A1 Olabarria, Celia
A1 Martinez Sánchez, Joaquín
A2 Ingeniería CartograficaGeodesica y Fotogrametria
K1 Ecología. Medio ambiente
K1 Macroalgae
K1 Intertidal rocky shore
K1 Convolutional neural networks
K1 Image processing
K1 Semantic segmentation
AB 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.
PB Taylor & Francis
SN 1366-5901
LK https://hdl.handle.net/10612/17918
UL https://hdl.handle.net/10612/17918
NO 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
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 04-jun-2024