Although recently several foundation models for satellite remote sensing imagery have been proposed, they fail to address major challenges of real/operational applications. Indeed, embeddings that don't take into account the spectral, spatial and temporal dimensions of the data as well as the irregular or unaligned temporal sampling are of little use for most real world uses. As a consequence, we propose an ALIgned Sits Encoder (ALISE), a novel approach that leverages the spatial, spectral, and temporal dimensions of irregular and unaligned SITS while producing aligned latent representations. Unlike SSL models currently available for SITS, ALISE incorporates a flexible query mechanism to project the SITS into a common and learned temporal projection space. Additionally, thanks to a multi-view framework, we explore integration of instance discrimination along a masked autoencoding task to SITS. The quality of the produced representation is assessed through three downstream tasks: crop segmentation (PASTIS), land cover segmentation (MultiSenGE), and a novel crop change detection dataset. Furthermore, the change detection task is performed without supervision. The results suggest that the use of aligned representations is more effective than previous SSL methods for linear probing segmentation tasks.

翻译：尽管近期已提出若干卫星遥感影像基础模型，但这些模型未能解决实际/业务应用中的主要挑战。事实上，忽略数据光谱、空间与时间维度特性，且未考虑不规则或未对齐时间采样的嵌入表示，对大多数现实应用场景价值有限。为此，我们提出对齐式卫星图像时间序列编码器（ALISE）——一种创新方法，在生成对齐潜在表征的同时，充分挖掘不规则与未对齐卫星图像时间序列的空间、光谱及时间维度信息。与当前卫星图像时间序列可用的自监督学习模型不同，ALISE通过灵活的查询机制将序列投影至统一的习得时间投影空间。此外，借助多视图框架，我们探索了在掩码自编码任务中融入实例判别机制的方法。通过三项下游任务评估生成表征的质量：作物分割（PASTIS）、土地覆盖分割（MultiSenGE）以及新型作物变化检测数据集。其中变化检测任务在无监督条件下完成。结果表明，在线性探测分割任务中，采用对齐表征的方法比现有自监督学习方法更具效能。