@conference{10902/24387,
year = {2021},
url = {http://hdl.handle.net/10902/24387},
abstract = {The monitoring of threatened habitats is a key objective of European environmental policy. Due to the high cost of current field-based habitat mapping techniques there is a strong research interest in proposing solutions that reduce the cost of habitat monitoring through increasing their level of automation. Our work is motivated by the opportunities that recent advances in machine learning and Unmanned Aerial Vehicles (UAVs) offer to the habitat monitoring problem. In this paper, a deep learning based solution is proposed to classify four priority Irish habitats types present in the Maharees (Ireland) using UAV aerial imagery. The proposed method employs Convolutional Neural Networks (CNNs) to classify multi-temporal multi-spectral images of the study area corresponding to three different dates in 2020, obtaining an overall classification accuracy of 93%. A comparison of the proposed method with a multi-spectral 2D-CNN model demonstrates the advantage of including temporal information enabled by the proposed multi-temporal multi-spectral CNN model.},
organization = {This project has received funding from the European Union’s Horizon
2020 research and innovation programme under the Marie Skłodowska-Curie
grant agreement No. 847402.},
publisher = {Institute of Electrical and Electronics Engineers, Inc.},
publisher = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2021), Brussels, Belgium, 2021, 2556-2559},
title = {Monitoring threatened irish habitats using multi-temporal multispectral aerial imagery and convolutional neural networks},
author = {Pérez Carabaza, Sara and Boydell, Oisín and O'Connell, Jerome},
}