FUSAR-KLIP: Towards Multimodal Foundation Models for Remote Sensing
It addresses a gap in remote sensing for SAR data, enabling all-day, all-weather scene understanding, though it is incremental by extending existing multimodal methods to a new domain.
The paper tackles the lack of multimodal foundation models for synthetic aperture radar (SAR) imagery by proposing FUSAR-KLIP, which includes a large-scale dataset with geographic information and a model achieving leading performance in tasks like object counting and land-cover classification across 11 downstream benchmarks.
Cross-modal artificial intelligence has garnered widespread attention in recent years, achieving significant progress in the study of natural images. However, existing methods are mostly designed for RGB imagery, leaving a significant gap in modeling synthetic aperture radar (SAR) imagery. SAR, with its all-day, all-weather imaging capabilities, plays an irreplaceable role in remote sensing scene understanding. To address this gap, this paper proposes FUSAR-KLIP, the first universal SAR multimodal foundational model, along with reusable data and evaluation baselines. Specifically: (1) This work introduces the critical yet long-overlooked attribute of geographic information into remote sensing research, constructing FUSAR-GEOVL-1M (the first large-scale SAR dataset with complete geographic projection properties), covering multiple satellite platforms, 120,000 images, and 135 cities. (2) Aligned structured text is generated through a hierarchical cognitive chain-of-thought (HCoT), providing more than one million multi-dimensional semantic annotations of landforms, regional functions, target attributes, and spatial relationships. (3) We design a Self-Consistent Iterative Optimization mechanism that continuously enhances cross-modal alignment through a self-supervised closed loop of contrastive, matching, and reconstruction learning on a transferable multimodal encoder. (4) A unified evaluation benchmark is established across 11 representative downstream vision and vision-language tasks, with comparisons against 14 leading foundation models, where FUSAR-KLIP demonstrates leading performance, particularly in object counting and land-cover classification. We expect that FUSAR-KLIP's large-scale multimodal data, transferable model architecture, and comprehensive experimental benchmark will significantly advance the development of SAR multimodal baseline models.