Habitat classification using convolutional neural networks and multitemporal multispectral aerial imagery
| Autor: | Sara Perez-Carabaza, Oisín Boydell, Jerome O'Connell |
|---|---|
| Přispěvatelé: | Universidad de Cantabria |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Contextual image classification
Computer science business.industry Vegetation classification Multispectral image Pattern recognition Multitemporal imagery Convolutional neural network Associative array Field (computer science) Data modeling Habitat mapping Threatened species General Earth and Planetary Sciences Convolutional neural networks Artificial intelligence business Unmanned aerial vehicle imagery |
| Zdroj: | Journal of Applied Remote Sensing, 2021, 15(4), 042406 UCrea Repositorio Abierto de la Universidad de Cantabria Universidad de Cantabria (UC) |
| Popis: | The monitoring of threatened habitats is a key objective of European environmental policies. Due to the high cost of current field-based habitat mapping techniques, there is keen interest in proposing solutions that can reduce cost through increased levels of automation. Our study aims to propose a habitat mapping solution that benefits both from the merits of convolutional neural networks (CNNs) for image classification tasks, as well as from the high spatial, spectral, and multitemporal unmanned aerial vehicle image data, which shows great potential for accurate vegetation classification. The proposed CNN-based method uses multitemporal multispectral aerial imagery for the classification of threatened coastal habitats in the Maharees (Ireland) and shows a high level of classification accuracy. This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie Grant Agreement No. 847402. The authors would like to thank the EPA-funded iHabiMap project for providing the data used in this work. We thank the anonymous reviewers whose comments and suggestions helped improve and clarify this manuscript. The authors declare no conflicts of interest |
| Databáze: | OpenAIRE |
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