论文标题
篡改VAE用于改进卫星图像时间序列分类
Tampered VAE for Improved Satellite Image Time Series Classification
论文作者
论文摘要
据信，空间和颞高分辨率卫星图像时间序列（坐着）的空间和暂时性高分辨率图像映射的空前可用性被认为需要深度学习体系结构来适应两个维度引起的挑战。最近最新的深度学习模型通过堆叠空间和时间编码器显示出令人鼓舞的结果。但是，我们提出了一个仅在时间维度上运行的金字塔时间序列变压器（PTST），即忽略空间尺寸，可以产生较高的结果，而GPU记忆消耗量的急剧降低和易于扩展性。此外，我们通过提出一个对分类友好的VAE框架，将其引入潜在空间，并可以促进其中的线性可分离性，从而增强其进行半监督学习。因此，潜在空间的一些主要轴可以解释原始数据的大多数差异。同时，使用建议的调整的VAE框架可以将竞争性分类性能保持为纯粹的歧视性效果，而当使用$ 40 \％$的标记数据时。我们希望所提出的框架可以作为农作物分类的基线，并以其模块化和简单性。
The unprecedented availability of spatial and temporal high-resolution satellite image time series (SITS) for crop type mapping is believed to necessitate deep learning architectures to accommodate challenges arising from both dimensions. Recent state-of-the-art deep learning models have shown promising results by stacking spatial and temporal encoders. However, we present a Pyramid Time-Series Transformer (PTST) that operates solely on the temporal dimension, i.e., neglecting the spatial dimension, can produce superior results with a drastic reduction in GPU memory consumption and easy extensibility. Furthermore, we augment it to perform semi-supervised learning by proposing a classification-friendly VAE framework that introduces clustering mechanisms into latent space and can promote linear separability therein. Consequently, a few principal axes of the latent space can explain the majority of variance in raw data. Meanwhile, the VAE framework with proposed tweaks can maintain competitive classification performance as its purely discriminative counterpart when only $40\%$ of labelled data is used. We hope the proposed framework can serve as a baseline for crop classification with SITS for its modularity and simplicity.
