Hyperspectral image fusion via unsupervised reversible network with adaptive scale-aware convolution

Hyperspectral images generally suffer from low spatial resolution, limiting their utility in fine-scale applications. To address the limitations of existing unsupervised fusion methods in adapting to spatial heterogeneity and scale effects that often lead to blurred details and reduced target discrimination, we propose an unsupervised adaptive scale-aware detail feature extraction network (UASNet), which introduces a structure-adaptive mechanism and degradation-aware modeling to effectively harmonize the representation consistency of multiscale ground objects without paired training samples. The network consists of three stages: the prior information mining stage, the spectral channel mapping stage, and the detail feature fusion stage. Specifically, an adaptive scale-aware convolution is embedded within a reversible detail extraction module to capture features of objects with varying scales and geometries, thereby preserving fine textures and structural integrity. Furthermore, driven by the requirements of real-world application scenarios, spectral and spatial branches are constructed to learn the respective degradation priors, which are integrated into the loss function to realize a fully unsupervised fusion framework. Extensive experiments on both simulated and real datasets demonstrate the superior performance of the proposed method compared with that of state-of-the-art approaches. Moreover, even without ground truth data, the downstream classification results indirectly validate that UASNet exhibits strong potential for real-world applications.