DETECTING LAND COVER CHANGES BETWEEN SATELLITE IMAGE TIME SERIES BY EXPLOITING SELF-SUPERVISED REPRESENTATION LEARNING CAPABILITIES

被引：1

作者：

Adebayo, Adebowale Daniel ^{[1
,2
]}

Pelletier, Charlotte ^{[1
]}

Lang, Stefan ^{[2
]}

Valero, Silvia ^{[3
]}

机构：

[1] Univ Bretagne Sud, IRISA, UMR CNRS 6074, Vannes, France

[2] Salzburg Univ, Dept Geoinformat, A-5020 Salzburg, Austria

[3] Univ Toulouse, CESBIO UMR CNRS 5126, Toulouse, France

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

land cover change detection; satellite image time series; self-supervised learning; contrastive learning;

D O I：

10.1109/IGARSS52108.2023.10281594

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

This work studies change detection from satellite image time series (SITS) with a proposed framework that leverages SITS using self-supervised learning. Experimental evaluation conducted on a study area in southwestern France demonstrates the effectiveness of the approach, with varying quantities of labeled training data. The results highlight the potential of self-supervised learning in producing accurate change detection maps for land cover analysis.

引用

页码：7168 / 7171

页数：4

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