用深度模型+贝叶斯隐马尔可夫，提升多年作物分类一致性。
Bayesian Modelling of Multi-Year Crop Type Classification Using Deep Neural Networks and Hidden Markov Models
- 融合Transformer的深度网络与隐马尔可夫模型，捕捉多年作物序列模式。
- 在6年、47类作物数据上，模型准确率显著提升，F1得分更高。
- 适合需要长期土地利用变化分析的研究者使用。
年度土地覆盖图的时间一致性对建模土地覆盖演变至关重要。本文提出一种新方法，结合深度学习与贝叶斯建模，利用基于Transformer编码器（TE）的深度神经网络与隐马尔可夫模型（HMM）联合处理逐年卫星影像时间序列（SITS）。该方法旨在同时捕捉i）逐年SITS中的复杂时间相关性，以及ii）多年作物类型序列中的特定模式。通过在顶层构建级联分类的HMM层，识别出稳定的年度作物类型序列。在包含47种作物类型、6年哨兵-2（Sentinel-2）影像数据的多年度作物分类数据集上验证，建模时间一致性显著提升预测性能。结果表明，引入HMM后整体性能与F1分数均得到增强，验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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