PAKAN: Pixel Adaptive Kolmogorov-Arnold Network Modules for Pansharpening
This work addresses pansharpening for remote sensing applications, presenting an incremental improvement by adapting existing KAN methods for dynamic inference.
The paper tackled the problem of pansharpening by proposing a Pixel Adaptive Kolmogorov-Arnold Network framework to dynamically model spatial-spectral fusion, resulting in significantly enhanced network performance as demonstrated in experiments.
Pansharpening aims to fuse high-resolution spatial details from panchromatic images with the rich spectral information of multispectral images. Existing deep neural networks for this task typically rely on static activation functions, which limit their ability to dynamically model the complex, non-linear mappings required for optimal spatial-spectral fusion. While the recently introduced Kolmogorov-Arnold Network (KAN) utilizes learnable activation functions, traditional KANs lack dynamic adaptability during inference. To address this limitation, we propose a Pixel Adaptive Kolmogorov-Arnold Network framework. Starting from KAN, we design two adaptive variants: a 2D Adaptive KAN that generates spline summation weights across spatial dimensions and a 1D Adaptive KAN that generates them across spectral channels. These two components are then assembled into PAKAN 2to1 for feature fusion and PAKAN 1to1 for feature refinement. Extensive experiments demonstrate that our proposed modules significantly enhance network performance, proving the effectiveness and superiority of pixel-adaptive activation in pansharpening tasks.