农田PAtteRNS：用于卫星影像时间序列数据中作物分割的并行维度注意力网络与数据集差异关注
Cropland PAtteRNS: Parallel Dimensional Attention Networks and Attention to Dataset Disparity for Crop Segmentation in Satellite Imagery Time Series Data
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中文总结 AI 辅助
提出PAtteRNS混合Transformer-卷积模型，分别对时间、光谱、空间维度使用自注意力，在PASTIS和MTLCC数据集上超越现有模型，并揭示数据集类别分组和瓦片尺寸对公平比较的影响。
中文摘要 AI 辅助
卫星影像时间序列数据集和用于农田分割的前沿架构从未如此丰富。然而，在这场淘金热中，两个领域的重要事实都被忽视了，因为对最新颖概念或最大数据集的追求将更精细的细节推到了一边。在本文中，我们提出了混合Transformer-卷积模型——农田并行注意力与细化分割网络（PAtteRNS），这是首个分别对Sentinel-2多光谱SITS数据的时间、光谱和空间方面使用自注意力机制的模型。为了在我们提出的模型中实现完全分解的注意力，我们引入了一种新颖的并行Transformer架构，该架构显著降低了三重分解自注意力的计算复杂度。我们通过深入的消融研究验证了我们的架构，并在流行的PASTIS和MTLCC数据集的多个瓦片尺寸变体上，分析了我们的模型与最先进的作物分割模型的性能。我们的研究结果表明，我们的模型在作物类别分割任务中优于所有其他模型，这在多个重要的分割指标上得到了验证，尤其是在经常未被充分报告的田块边界分割质量方面表现尤为突出，我们使用边界IoU指标进行评估。我们还发现，数据集中有缺陷的类别分组可能对模型性能产生显著的负面影响，并报告说作物分割数据集的不同瓦片尺寸变体产生的结果彼此不可比较，这使得在不同瓦片尺寸上训练的模型性能之间的公平比较无效。基于这些发现，我们建议需要进一步的工作来标准化构建SITS作物分割数据集的最佳实践，并为理想的模型性能实现未来的动态瓦片尺寸调整。
英文摘要
The landscape of satellite imagery time series datasets and boundary-pushing architectures for cropland segmentation has never been richer. However, in this gold rush, important truths are being missed on both fronts, as a drive for the most novel concepts or the largest datasets pushes finer details to the side. In this paper, we present our hybrid transformer-convolutional model, Cropland Parallel Attention and Refinement Network for Segmentation (PAtteRNS), the first model to use self-attention mechanisms separately for each of the temporal, spectral, and spatial aspects of Sentinel-2 multispectral SITS data. To achieve fully-factorised attention in our proposed model, we introduce a novel parallel transformer architecture which significantly reduces the computational complexity of triple-factorised self-attention. We validate our architecture with an in-depth ablation study, and analyse the performance of our model against state-of-the-art crop segmentation models on multiple tile-size variants of the popular PASTIS and MTLCC datasets. Our findings show our model to outperform all others in the task of crop class segmentation, verified across multiple important segmentation metrics, with especially strong performance against compared models seen in the often under-reported parcel delineation quality, for which we use the Boundary IoU metric. We also find that flawed class groupings within datasets can have a significant negative impact on model performance, and report that alternate tile-size variants of crop segmentation datasets produce results incomparable to one-another, invalidating fair comparison between model performance when trained on different tile-sizes. Based on these findings, we suggest further work is required to standardise best practices when constructing SITS crop segmentation datasets, and to enable future dynamic-tile-sizing for ideal model performance.
发表机构
- Swansea University（斯旺西大学）
机构由 AI 辅助整理，请以论文原文为准。
