Adaptive Rectangular Convolution for Remote Sensing Pansharpening
Journal:
arXiv
Published Date:
Mar 1, 2025
Abstract
Recent advancements in convolutional neural network (CNN)-based techniques
for remote sensing pansharpening have markedly enhanced image quality. However,
conventional convolutional modules in these methods have two critical
drawbacks. First, the sampling positions in convolution operations are confined
to a fixed square window. Second, the number of sampling points is preset and
remains unchanged. Given the diverse object sizes in remote sensing images,
these rigid parameters lead to suboptimal feature extraction. To overcome these
limitations, we introduce an innovative convolutional module, Adaptive
Rectangular Convolution (ARConv). ARConv adaptively learns both the height and
width of the convolutional kernel and dynamically adjusts the number of
sampling points based on the learned scale. This approach enables ARConv to
effectively capture scale-specific features of various objects within an image,
optimizing kernel sizes and sampling locations. Additionally, we propose ARNet,
a network architecture in which ARConv is the primary convolutional module.
Extensive evaluations across multiple datasets reveal the superiority of our
method in enhancing pansharpening performance over previous techniques.
Ablation studies and visualization further confirm the efficacy of ARConv.
