The temporal consistency of yearly land-cover maps is of great importance to model the evolution and change of the land cover over the years. In this paper, we focus the attention on a novel approach to classification of yearly satellite image time series (SITS) that combines deep learning with Bayesian modelling, using Hidden Markov Models (HMMs) integrated with Transformer Encoder (TE) based DNNs. The proposed approach aims to capture both i) intricate temporal correlations in yearly SITS and ii) specific patterns in multiyear crop type sequences. It leverages the cascade classification of an HMM layer built on top of the TE, discerning consistent yearly crop-type sequences. Validation on a multiyear crop type classification dataset spanning 47 crop types and six years of Sentinel-2 acquisitions demonstrates the importance of modelling temporal consistency in the predicted labels. HMMs enhance the overall performance and F1 scores, emphasising the effectiveness of the proposed approach.

翻译：年度土地覆盖图的时间一致性对于模拟多年间土地覆盖的演变与变化至关重要。本文聚焦于一种新颖的年度卫星图像时间序列分类方法，该方法将深度学习与贝叶斯建模相结合，利用隐马尔可夫模型与基于Transformer编码器的深度神经网络进行集成。所提出的方法旨在同时捕捉：i) 年度卫星图像时间序列中复杂的时序相关性，以及 ii) 多年作物类型序列中的特定模式。该方法利用了构建于Transformer编码器之上的HMM层进行级联分类，以识别一致的年度作物类型序列。在一个涵盖47种作物类型、包含六年Sentinel-2影像的多年作物类型分类数据集上的验证表明，对预测标签进行时间一致性建模具有重要意义。隐马尔可夫模型提升了整体性能与F1分数，突显了所提方法的有效性。