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Optimised U-Net for Land Use–Land Cover Classification Using Aerial Photography by Andrew Clark; Stuart Phinn; Peter Scarth is a Environmental Science article available to read on EtoBox.

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Abstract Convolutional Neural Networks (CNN) consist of various hyper-parameters which need to be specified or can be altered when defining a deep learning architecture. There are numerous studies which have tested different types of networks (e.g. U-Net, DeepLabv3+) or created new architectures, benchmarked against well-known test datasets. However, there is a lack of real-world mapping applications demonstrating the effects of changing network hyper-parameters on model performance for land use and land cover (LULC) semantic segmentation. In this paper, we analysed the effects on training time and classification accuracy by altering parameters such as the number of initial convolutional filters, kernel size, network depth, kernel initialiser and activation functions, loss and loss optimiser functions, and learning rate. We achieved this using a well-known top performing architecture, the U-Net, in conjunction with LULC training data and two multispectral aerial images from North Queensland, Australia. A 2018 image was used to train and test CNN models with different parameters and a 2015 image was used for assessing the optimised parameters. We found more complex models with a lar

Who reads Optimised U-Net for Land Use–Land Cover Classification Using Aerial Photography?

Author

Andrew Clark; Stuart Phinn; Peter Scarth

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)