DiffPR-Net: Few-Shot Remote Sensing Scene Classification Based on Generative Diffusion and Prototype Rectified Model

Abstract

Few-shot remote sensing scene classification (FSRSSC) aims to identify unseen scene classes from limited labeled samples, facing the challenge of accurately modeling data distribution and preserving image details in complex backgrounds with high intraclass variance and interclass similarity. To address this challenge, we propose a novel diffusion prototype rectified network (DiffPR-Net), which is comprised of three core modules: diffusion augmentation (DA), dual attention fusion module (DAFM), and prototype rectified module (PRM). The DA is constructed to generate high-quality remote sensing images with the objective of augmenting the training dataset. Besides, the DAFM facilitates the model to focus discriminative regions by transmitting highly fused image detail features from higher to lower layers. What is more, the PRM addresses prototype deviation by adaptively assigning temporary labels to unlabeled data based on prediction confidence, thereby correcting the initial prototypes. Experiments indicate that our proposed method is highly promising, achieving competitive or state-of-the-art (SOTA) classification performance while addressing the scarcity of remotely sensed data and enhancing focus on discriminative regions.