Probabilistic von Mises–Fisher representation learning for few-shot remote sensing scene classification

Abstract

Few-Shot Remote Sensing Image Scene Classification (FS-RSISC) remains challenging due to scarce labeled data, severe intra-class variation, and high inter-class similarity inherent in remote sensing imagery. To address these issues, we propose a probabilistic reformulation of FS-RSISC that moves beyond rigid point-based prototypes by modeling each class as a probability distribution over the hyperspherical feature space. Specifically, we recast category representation as a density estimation problem on the unit hypersphere and adopt the von Mises–Fisher (vMF) distribution to capture both semantic uncertainty and feature diversity. We develop a Bayesian-inspired parameter estimation pipeline that initializes vMF parameters using CLIP-derived embeddings as semantic priors and progressively refines them via data augmentation and a Mixture-of-Experts-based adapter tuning mechanism, enabling robust adaptation under few-shot constraints. Furthermore, we propose two loss functions specifically formulated for the vMF distribution that exploit the geometry of the hypersphere: an Intra-Class Compactness (ICC) loss that leverages the concentration parameter to impose angularly adaptive regularization and enhance feature concentration around semantic centroids, and an Inter-Class Repulsion (ICR) loss that explicitly maximizes angular separation between class centroids to mitigate inter-class overlap. Extensive experiments on three standard benchmarks demonstrate that our method consistently outperforms existing state-of-the-art approaches, establishing a principled and effective framework for FS-RSISC.