Papers › ViTs for SITS: Vision Transformers for Satellite Image Time Series

ViTs for SITS: Vision Transformers for Satellite Image Time Series

12 Jan 2023CVPR 2023 1arXiv:2301.04944archive 2025-07-28

Michail Tarasiou, Erik Chavez, Stefanos Zafeiriou

In this paper we introduce the Temporo-Spatial Vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the Vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed by a factorized temporo-spatial encoder. We argue, that in contrast to natural images, a temporal-then-spatial factorization is more intuitive for SITS processing and present experimental evidence for this claim. Additionally, we enhance the model's discriminative power by introducing two novel mechanisms for acquisition-time-specific temporal positional encodings and multiple learnable class tokens. The effect of all novel design choices is evaluated through an extensive ablation study. Our proposed architecture achieves state-of-the-art performance, surpassing previous approaches by a significant margin in three publicly available SITS semantic segmentation and classification datasets. All model, training and evaluation codes are made publicly available to facilitate further research.

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Attention michaeltrs/DeepSatModels/models/TSViT/TSViTcls.py official repository ran Apache-2.0 (permissive) · 7ff8cc1149fcdb55 · report
TSViTcls michaeltrs/DeepSatModels/models/TSViT/TSViTcls.py official repository ran Apache-2.0 (permissive) · 92778a46d60fa611 · report
Transformer michaeltrs/DeepSatModels/models/TSViT/TSViTcls.py official repository ran Apache-2.0 (permissive) · 3c8abb5d4b170a1d · report
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Tasks

Semantic SegmentationTime SeriesTime Series Analysis

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformerVision Transformer

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