Delineating farm boundaries through segmentation of satellite images is a fundamental step in many agricultural applications. The task is particularly challenging for smallholder farms, where accurate delineation requires the use of high resolution (HR) imagery which are available only at low revisit frequencies (e.g., annually). To support more frequent (sub-) seasonal monitoring, HR images could be combined as references (ref) with low resolution (LR) images -- having higher revisit frequency (e.g., weekly) -- using reference-based super-resolution (Ref-SR) methods. However, current Ref-SR methods optimize perceptual quality and smooth over crucial features needed for downstream tasks, and are unable to meet the large scale-factor requirements for this task. Further, previous two-step approaches of SR followed by segmentation do not effectively utilize diverse satellite sources as inputs. We address these problems through a new approach, $\textbf{SEED-SR}$, which uses a combination of conditional latent diffusion models and large-scale multi-spectral, multi-source geo-spatial foundation models. Our key innovation is to bypass the explicit SR task in the pixel space and instead perform SR in a segmentation-aware latent space. This unique approach enables us to generate segmentation maps at an unprecedented 20$\times$ scale factor, and rigorous experiments on two large, real datasets demonstrate up to $\textbf{25.5}$ and $\textbf{12.9}$ relative improvement in instance and semantic segmentation metrics respectively over approaches based on state-of-the-art Ref-SR methods.

翻译：通过卫星图像分割划定农田边界是众多农业应用中的基础步骤。对于小农户农田而言，该任务尤为困难，因为精确划定需要使用高分辨率（HR）影像，而此类影像通常仅能以较低的重复观测频率获取（例如每年一次）。为支持更频繁的（亚）季节性监测，可将高分辨率影像作为参考（ref）与具有更高重复观测频率（例如每周）的低分辨率（LR）影像结合，采用基于参考的超分辨率（Ref-SR）方法进行处理。然而，当前Ref-SR方法主要优化感知质量，会平滑下游任务所需的关键特征，且无法满足该任务对大尺度因子的需求。此外，先前先进行超分辨率再分割的两步法未能有效利用多样化的卫星数据源作为输入。我们通过一种新方法——$\textbf{SEED-SR}$——来解决这些问题，该方法结合了条件潜在扩散模型与大规模多光谱、多源地理空间基础模型。我们的核心创新在于绕过像素空间中的显式超分辨率任务，转而在感知分割的潜在空间中进行超分辨率处理。这一独特方法使我们能够以前所未有的20$\times$尺度因子生成分割图，在两个大规模真实数据集上的严格实验表明，相较于基于最先进Ref-SR方法的方法，我们在实例分割和语义分割指标上分别实现了高达$\textbf{25.5}$和$\textbf{12.9}$的相对提升。