Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks
Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of agricultural parcels has major economic and environmental implications. While researchers have explored this problem for s...
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| Vydané v: | Proceedings / IEEE International Conference on Computer Vision s. 4852 - 4861 |
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01.10.2021
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| Abstract | Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of agricultural parcels has major economic and environmental implications. While researchers have explored this problem for single images, we argue that the complex temporal patterns of crop phenology are better addressed with temporal sequences of images. In this paper, we present the first end-to-end, single-stage method for panoptic segmentation of Satellite Image Time Series (SITS). This module can be combined with our novel image sequence encoding network which relies on temporal self-attention to extract rich and adaptive multi-scale spatiotemporal features. We also introduce PASTIS, the first open-access SITS dataset with panoptic annotations. We demonstrate the superiority of our encoder for semantic segmentation against multiple competing architectures, and set up the first state-of-the-art of panoptic segmentation of SITS. Our implementation and PASTIS are publicly available. |
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| AbstractList | Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of agricultural parcels has major economic and environmental implications. While researchers have explored this problem for single images, we argue that the complex temporal patterns of crop phenology are better addressed with temporal sequences of images. In this paper, we present the first end-to-end, single-stage method for panoptic segmentation of Satellite Image Time Series (SITS). This module can be combined with our novel image sequence encoding network which relies on temporal self-attention to extract rich and adaptive multi-scale spatiotemporal features. We also introduce PASTIS, the first open-access SITS dataset with panoptic annotations. We demonstrate the superiority of our encoder for semantic segmentation against multiple competing architectures, and set up the first state-of-the-art of panoptic segmentation of SITS. Our implementation and PASTIS are publicly available. |
| Author | Landrieu, Loic Fare Garnot, Vivien Sainte |
| Author_xml | – sequence: 1 givenname: Vivien Sainte surname: Fare Garnot fullname: Fare Garnot, Vivien Sainte email: vivien.sainte-fare-garnot@ign.fr organization: Univ. Gustave Eiffel, ENSG, IGN,LASTIG,Saint-Mande,France,F-94160 – sequence: 2 givenname: Loic surname: Landrieu fullname: Landrieu, Loic email: loic.landrieu@ign.fr organization: Univ. Gustave Eiffel, ENSG, IGN,LASTIG,Saint-Mande,France,F-94160 |
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| Snippet | Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise... |
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| SubjectTerms | Computer vision Economics Feature extraction grouping and shape Image segmentation Satellites Segmentation Semantics Time series analysis Vision + other modalities Vision applications and systems |
| Title | Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks |
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