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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Vydáno v:Proceedings / IEEE International Conference on Computer Vision s. 4852 - 4861
Hlavní autoři: Fare Garnot, Vivien Sainte, Landrieu, Loic
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2021
Témata:
Computer vision
Economics
Feature extraction
grouping and shape
Image segmentation
Satellites
Segmentation
Semantics
Time series analysis
Vision + other modalities
Vision applications and systems
ISSN:2380-7504
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Popis
Shrnutí: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.
ISSN:2380-7504
DOI:10.1109/ICCV48922.2021.00483