Abstract
The rapid expansion of multi-source satellite imagery drives innovation in Earth observation, opening unprecedented opportunities for Remote Sensing Foundation Models to harness diverse data. However, many existing models remain constrained by fixed spatial resolutions and patch sizes, limiting their ability to fully exploit the heterogeneous spatial characteristics inherent in satellite imagery. To address these challenges, we propose FlexiMo, a flexible remote sensing foundation model that endows the pre-trained model with the flexibility to adapt to arbitrary spatial resolutions. Central to FlexiMo is a spatial resolution-aware module that employs a parameter-free alignment embedding mechanism to dynamically recalibrate patch embeddings based on the input image's resolution and dimensions. This design not only preserves critical token characteristics and ensures multi-scale feature fidelity but also enables efficient feature extraction without requiring modifications to the underlying network architecture. In addition, FlexiMo incorporates a lightweight channel adaptation module that leverages prior spectral information from sensors. This mechanism allows the model to process images with varying numbers of channels while maintaining the data's intrinsic physical properties. Extensive experiments on diverse multimodal, multi-resolution, and multi-scale datasets demonstrate that FlexiMo significantly enhances model generalization and robustness. In particular, our method achieves outstanding performance across a range of downstream tasks, including scene classification, land cover classification, urban building segmentation, and cloud detection. By enabling parameter-efficient and physically consistent adaptation, FlexiMo paves the way for more adaptable and effective foundation models in real-world remote sensing applications.
Abstract (translated)
多源卫星图像的快速扩展推动了地球观测领域的创新，为遥感基础模型利用多样化数据提供了前所未有的机遇。然而，许多现有的模型仍然受到固定空间分辨率和补丁大小的限制，这限制了它们充分利用卫星图像中固有的异构空间特征的能力。为了解决这些挑战，我们提出了FlexiMo，这是一种灵活的遥感基础模型，它赋予预训练模型适应任意空间分辨率的灵活性。FlexiMo的核心是一个基于空间分辨率感知模块，该模块采用了一种无参数对齐嵌入机制，根据输入图像的分辨率和尺寸动态校准补丁嵌入。这种设计不仅保留了关键标记特性，并确保了多尺度特征的一致性，而且还能够在不修改底层网络架构的情况下实现高效的特征提取。 此外，FlexiMo集成了一个轻量级通道适应模块，利用传感器中的先验光谱信息。这一机制使模型能够处理不同通道数量的图像，同时保持数据的基本物理属性不变。在多种多模态、多分辨率和多尺度的数据集上进行广泛的实验表明，FlexiMo显著增强了模型的一般性和鲁棒性。特别是在场景分类、土地覆盖分类、城市建筑物分割以及云检测等一系列下游任务中，我们的方法取得了出色的性能。 通过实现参数效率高且物理属性一致的适应能力，FlexiMo为在现实世界的遥感应用中的更灵活和有效的基础模型的发展铺平了道路。
URL
https://arxiv.org/abs/2503.23844
