SHARPENING THE 20 M BANDS OF SENTINEL-2 IMAGE USING AN UNSUPERVISED CONVOLUTIONAL NEURAL NETWORK

Rannsóknarafurð: Framlag á ráðstefnuVísindagreinritrýni

Útdráttur

This paper proposes a novel method for sharpening the 20 m bands of the multispectral images acquired by the Sentinel- 2 (S2) constellation. We formulate the S2 sharpening as an inverse problem and solve it using an unsupervised convolutional neural network (CNN), called S2UCNN. The proposed method extends the deep image prior provided by a CNN structure with S2 domain knowledge. We incorporate a modulation transfer function-based degradation model as a network layer. We add the 10 m bands to both the network input and output to take advantage of the multitask learning. Experimental results with a real S2 dataset show that the proposed method outperforms the competitive methods on reduced-resolution data and gives very high quality sharpened image on full-resolution data.

Upprunalegt tungumálEnska
Síður2875-2878
Síðufjöldi4
DOI
ÚtgáfustaðaÚtgefið - 2021
Viðburður2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgía
Tímalengd: 12 júl. 202116 júl. 2021

Ráðstefna

Ráðstefna2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Land/YfirráðasvæðiBelgía
Borg/bærBrussels
Tímabil12/07/2116/07/21

Athugasemd

Publisher Copyright:
© 2021 IEEE.

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