Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based land cover classification
This work addresses a domain-specific problem for ecology, agriculture, and land management by providing an incremental improvement in combining complementary satellite data sources.
The paper tackled the challenge of combining radar and optical satellite image time series for land cover classification by proposing a new neural architecture that integrates Sentinel-1 and Sentinel-2 data at the object level, achieving significant results in experiments on Reunion Island.
Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobility, land management planning and land cover monitoring. Many studies have been conducted using one of the two sources, but how to smartly combine the complementary information provided by radar and optical SITS is still an open challenge. In this context, we propose a new neural architecture for the combination of Sentinel-1 (S1) and Sentinel-2 (S2) imagery at object level, applied to a real-world land cover classification task. Experiments carried out on the Reunion Island, a overseas department of France in the Indian Ocean, demonstrate the significance of our proposal.