Fully convolutional neural nets in-the-wild

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dc.contributor.authorSimms, Daniel M.
dc.date.accessioned2020-10-22T10:15:49Z
dc.date.available2020-10-22T10:15:49Z
dc.date.freetoread2021-10-21
dc.date.issued2020-10-20
dc.description.abstractThe ground breaking performance of fully convolutional neural nets (FCNs) for semantic segmentation tasks has yet to be achieved for landcover classification, partly because a lack of suitable training data. Here the FCN8 model is trained and evaluated in real-world conditions, so called in-the-wild, for the classification of opium poppy and cereal crops at very high resolution (1 m). Densely labelled image samples from 74 Ikonos scenes were taken from 3 years of opium cultivation surveys for Helmand Province, Afghanistan. Models were trained using 1 km2 samples, subsampled patches and transfer learning. Overall accuracy was 88% for a FCN8 model transfer-trained on all three years of data and complex features were successfully grouped into distinct field parcels from the training data alone. FCNs can be trained end-to-end using variable sized input images for pixel-level classification that combines the spatial and spectral properties of target objects in a single operation. Transfer learning improves classifier performance and can be used to share information between FCNs, demonstrating their potential to significantly improve land cover classification more generally.en_UK
dc.identifier.citationSimms DM. (2020) Fully convolutional neural nets in-the-wild. Remote Sensing Letters, Volume 11, Issue 12, 2020, pp.1080-1089en_UK
dc.identifier.cris28145491
dc.identifier.issn2150-704X
dc.identifier.urihttps://doi.org/10.1080/2150704X.2020.1821120
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/15908
dc.language.isoenen_UK
dc.publisherTaylor and Franciesen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectFCN8en_UK
dc.subjectCNNsen_UK
dc.subjectOpium Poppyen_UK
dc.subjectLandcover Classificationen_UK
dc.subjectConvnetsen_UK
dc.subjectSemantic Segmentationen_UK
dc.titleFully convolutional neural nets in-the-wilden_UK
dc.typeArticleen_UK

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