Cited by 263Remote-Sensing Image ClassificationRemote Sensing in AgricultureAdvanced Neural Network Applications

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Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks

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Fare Garnot, Vivien Sainte, Landrieu, Loic·2021·2021 IEEE/CVF International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv48922.2021.00483

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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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Authors: Fare Garnot, V. S., & Landrieu, L.
Affiliations: Univ. Gustave Eiffel, ENSG, IGN,LASTIG,Saint-Mande,France,F-94160
Venue: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2021-10
Pages: 4852-4861
DOI: 10.1109/iccv48922.2021.00483
OpenAlex: W3185151088
Cited by: 242
References: 56
Citations by year: 2026: 49 · 2025: 67 · 2024: 60 · 2023: 43 · 2022: 20 · 2021: 3
FWCI: 54.60
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
arXiv (Cornell University) (submittedVersion)
HAL (submittedVersion, other-oa)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: panoptic segmentation, satellite image time series, crop phenology, agricultural parcels, temporal self-attention, spatio-temporal features, semantic segmentation

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Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks

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