# Adaptive Rectangular Convolution for Remote Sensing Pansharpening

arXiv: https://arxiv.org/abs/2503.00467 · HTML: https://mlsanity.com/paper/2503.00467/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Pixel-Adaptive Convolutional Neural Networks](https://mlsanity.com/paper/1904.05373/) (2019)
- [Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation](https://mlsanity.com/paper/2307.08388/) (2023)
- [Decoupled Dynamic Filter Networks](https://mlsanity.com/paper/2104.14107/) (2021)
- [Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition](https://mlsanity.com/paper/2006.11538/) (2020)
- [Content-Adaptive Non-Local Convolution for Remote Sensing Pansharpening](https://mlsanity.com/paper/2404.07543/) (2024)
- [CMT: Cross Modulation Transformer with Hybrid Loss for Pansharpening](https://mlsanity.com/paper/2404.01121/) (2024)

## Built on by (most cited first, 10 of 10)

- [Pansharpening for Thin-Cloud Contaminated Remote Sensing Images: A Unified Framework and Benchmark Dataset](https://mlsanity.com/paper/2603.14952/) (2026)
- [Cross-Scale Pansharpening via ScaleFormer and the PanScale Benchmark](https://mlsanity.com/paper/2603.00543/) (2026)
- [Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks](https://mlsanity.com/paper/2505.03522/) (2025)
- [RAPNet: A Receptive-Field Adaptive Convolutional Neural Network for Pansharpening](https://mlsanity.com/paper/2507.10461/) (2025)
- [SatFusion: A Unified Framework for Enhancing Remote Sensing Images via Multi-Frame and Multi-Source Images Fusion](https://mlsanity.com/paper/2510.07905/) (2025)
- [RDTE-UNet: A Boundary and Detail Aware UNet for Precise Medical Image Segmentation](https://mlsanity.com/paper/2511.01328/) (2025)
- [Physically Interpretable Multi-Degradation Image Restoration via Deep Unfolding and Explainable Convolution](https://mlsanity.com/paper/2511.10166/) (2025)
- [DMAConv: Dual Mask-Adaptive Convolution for Remote Sensing Pansharpening](https://mlsanity.com/paper/2512.08331/) (2025)
- [CC-Pan: Channel-wise Compression based Diffusion for Efficient Pan-Sharpening](https://mlsanity.com/paper/2602.04473/) (2026)
- [WGDnet: Wishart-guided Geometric-aware Deep Network for PolSAR Image Classification](https://mlsanity.com/paper/2607.23638/) (2026)

## Abstract (arXiv)

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.