Content-Adaptive Non-Local Convolution for Remote Sensing Pansharpening
Yule Duan Xiao Wu Haoyu Deng Liang-Jian Deng
Currently, machine learning-based methods for remote sensing pansharpening
have progressed rapidly. However, existing pansharpening methods often do not
fully exploit differentiating regional information in non-local spaces, thereby
limiting the effectiveness of the methods and resulting in redundant learning
parameters. In this paper, we introduce a so-called content-adaptive non-local
convolution (CANConv), a novel method tailored for remote sensing image
pansharpening. Specifically, CANConv employs adaptive convolution, ensuring
spatial adaptability, and incorporates non-local self-similarity through the
similarity relationship partition (SRP) and the partition-wise adaptive
convolution (PWAC) sub-modules. Furthermore, we also propose a corresponding
network architecture, called CANNet, which mainly utilizes the multi-scale
self-similarity. Extensive experiments demonstrate the superior performance of
CANConv, compared with recent promising fusion methods. Besides, we
substantiate the method's effectiveness through visualization, ablation
experiments, and comparison with existing methods on multiple test sets. The
source code is publicly available at https://github.com/duanyll/CANConv.