# UniDiff: Parameter-Efficient Adaptation of Diffusion Models for Land Cover Classification with Multi-Modal Remotely Sensed Imagery and Sparse Annotations Source: [UniDiff: Parameter-Efficient Adaptation of Diffusion Models for Land Cover Classification with Multi-Modal Remotely Sensed Imagery and Sparse Annotations]() Collection index: [llms.txt]() Data updated: 2025-12-03T05:02:38.198Z Authors: Yuzhen Hu, Saurabh Prasad Published: 2025-11-29T00:57:53.000Z ## Abstract Sparse annotations fundamentally constrain multimodal remote sensing: even recent state-of-the-art supervised methods such as MSFMamba are limited by the availability of labeled data, restricting their practical deployment despite architectural advances. ImageNet-pretrained models provide rich visual representations, but adapting them to heterogeneous modalities such as hyperspectral imaging (HSI) and synthetic aperture radar (SAR) without large labeled datasets remains challenging. We propose UniDiff, a parameter-efficient framework that adapts a single ImageNet-pretrained diffusion model to multiple sensing modalities using only target-domain data. UniDiff combines FiLM-based timestep-modality conditioning, parameter-efficient adaptation of approximately 5% of parameters, and pseudo-RGB anchoring to preserve pre-trained representations and prevent catastrophic forgetting. This design enables effective feature extraction from remote sensing data under sparse annotations. Our results with two established multi-modal benchmarking datasets demonstrate that unsupervised adaptation of a pre-trained diffusion model effectively mitigates annotation constraints and achieves effective fusion of multi-modal remotely sensed data. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()