Related papers: Self-supervised learning for joint SAR and multispectral land cover classification

Self-supervised learning for joint SAR and multispectral land cover classification

URL: http://arxiv.org/abs/2108.09075v1
Date: Fri, 20 Aug 2021 09:02:07 GMT
Title: Self-supervised learning for joint SAR and multispectral land cover classification
Authors: Antonio Montanaro, Diego Valsesia, Giulia Fracastoro, Enrico Magli
Abstract summary: We present a framework and specific tasks for self-supervised training of multichannel models. We show that the proposed self-supervised approach is highly effective at learning features that correlate with the labels for land cover classification.
Score: 38.8529535887097
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Self-supervised learning techniques are gaining popularity due to their capability of building models that are effective, even when scarce amounts of labeled data are available. In this paper, we present a framework and specific tasks for self-supervised training of multichannel models, such as the fusion of multispectral and synthetic aperture radar images. We show that the proposed self-supervised approach is highly effective at learning features that correlate with the labels for land cover classification. This is enabled by an explicit design of pretraining tasks which promotes bridging the gaps between sensing modalities and exploiting the spectral characteristics of the input. When limited labels are available, using the proposed self-supervised pretraining and supervised finetuning for land cover classification with SAR and multispectral data outperforms conventional approaches such as purely supervised learning, initialization from training on Imagenet and recent self-supervised approaches for computer vision tasks.

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