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Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks

Abstract : The Copernicus Sentinel-2 program now provides mul-tispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.
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https://hal.archives-ouvertes.fr/hal-01899024
Contributor : Rodrigo Caye Daudt Connect in order to contact the contributor
Submitted on : Friday, October 19, 2018 - 4:59:04 PM
Last modification on : Tuesday, January 18, 2022 - 3:28:04 PM
Long-term archiving on: : Sunday, January 20, 2019 - 1:04:38 PM

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  • HAL Id : hal-01899024, version 1

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Rodrigo Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau. Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks. IGARSS 2018, Jul 2018, Valencia, Spain. ⟨hal-01899024⟩

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