RAPNet: A Receptive-Field Adaptive Convolutional Neural Network for Pansharpening

• URL: http://arxiv.org/abs/2507.10461v3
• Date: Tue, 19 Aug 2025 10:20:31 GMT
• Title: RAPNet: A Receptive-Field Adaptive Convolutional Neural Network for Pansharpening
• Authors: Tao Tang, Chengxu Yang,
• Abstract summary: We introduce RAPNet, a new architecture that leverages content-adaptive convolution.<n>RAPNet employs the Receptive-field Adaptive Pansharpening Convolution (RAPConv), designed to produce spatially adaptive kernels responsive to local feature context.<n>The network integrates the Pansharpening Dynamic Feature Fusion (PAN-DFF) module, which incorporates an attention mechanism to achieve an optimal balance between spatial detail enhancement and spectral fidelity.
• Score: 2.746409982853943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pansharpening refers to the process of integrating a high resolution panchromatic (PAN) image with a lower resolution multispectral (MS) image to generate a fused product, which is pivotal in remote sensing. Despite the effectiveness of CNNs in addressing this challenge, they are inherently constrained by the uniform application of convolutional kernels across all spatial positions, overlooking local content variations. To overcome this issue, we introduce RAPNet, a new architecture that leverages content-adaptive convolution. At its core, RAPNet employs the Receptive-field Adaptive Pansharpening Convolution (RAPConv), designed to produce spatially adaptive kernels responsive to local feature context, thereby enhancing the precision of spatial detail extraction. Additionally, the network integrates the Pansharpening Dynamic Feature Fusion (PAN-DFF) module, which incorporates an attention mechanism to achieve an optimal balance between spatial detail enhancement and spectral fidelity. Comprehensive evaluations on publicly available datasets confirm that RAPNet delivers superior performance compared to existing approaches, as demonstrated by both quantitative metrics and qualitative assessments. Ablation analyses further substantiate the effectiveness of the proposed adaptive components.

Related papers

• RepSFNet : A Single Fusion Network with Structural Reparameterization for Crowd Counting [6.895355763564631]
  RepSFNet is a lightweight architecture designed for accurate and real-time crowd estimation.<n>RepSFNet achieves competitive accuracy while reducing inference latency by up to 34 percent compared to recent state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-01-28T08:33:36Z)
• RS-ISRefiner: Towards Better Adapting Vision Foundation Models for Interactive Segmentation of Remote Sensing Images [17.648922817109224]
  RS-ISRefiner is a novel click-based IIS framework tailored for remote sensing images.<n>It consistently outperforms state-of-the-art IIS methods in terms of segmentation accuracy, efficiency and interaction cost.
  arXiv  Detail & Related papers  (2025-11-30T04:12:43Z)
• GCRPNet: Graph-Enhanced Contextual and Regional Perception Network for Salient Object Detection in Optical Remote Sensing Images [68.33481681452675]
  We propose a graph-enhanced contextual and regional perception network (GCRPNet)<n>It builds upon the Mamba architecture to simultaneously capture long-range dependencies and enhance regional feature representation.<n>It performs adaptive patch scanning on feature maps processed via multi-scale convolutions, thereby capturing rich local region information.
  arXiv  Detail & Related papers  (2025-08-14T11:31:43Z)
• Residual Prior-driven Frequency-aware Network for Image Fusion [6.90874640835234]
  Image fusion aims to integrate complementary information across modalities to generate high-quality fused images.<n>We propose a Residual Prior-driven Frequency-aware Network, termed as RPFNet.
  arXiv  Detail & Related papers  (2025-07-09T10:48:00Z)
• A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation [1.833928124984226]
  GLCANet is a lightweight segmentation framework designed for UHR remote sensing imagery.<n>A self-attention mechanism enhances long-range dependencies, refines global features, and preserves local details for better semantic consistency.<n>A masked cross-attention mechanism also adaptively fuses global-local features, selectively enhancing fine-grained details while exploiting global context to improve segmentation accuracy.
  arXiv  Detail & Related papers  (2025-06-24T08:20:08Z)
• SAIP-Net: Enhancing Remote Sensing Image Segmentation via Spectral Adaptive Information Propagation [12.735064111733696]
  This paper introduces SAIP-Net, a novel frequency-aware segmentation framework.<n>SAIP-Net employs adaptive frequency filtering and multi-scale receptive field enhancement.<n>Experiments demonstrate significant performance improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-04-23T09:43:58Z)
• Optimal Transport Adapter Tuning for Bridging Modality Gaps in Few-Shot Remote Sensing Scene Classification [80.83325513157637]
  Few-Shot Remote Sensing Scene Classification (FS-RSSC) presents the challenge of classifying remote sensing images with limited labeled samples.<n>We propose a novel Optimal Transport Adapter Tuning (OTAT) framework aimed at constructing an ideal Platonic representational space.
  arXiv  Detail & Related papers  (2025-03-19T07:04:24Z)
• Double-Shot 3D Shape Measurement with a Dual-Branch Network for Structured Light Projection Profilometry [14.749887303860717]
  We propose a dual-branch Convolutional Neural Network (CNN)-Transformer network (PDCNet) to process different structured light (SL) modalities.<n>Within PDCNet, a Transformer branch is used to capture global perception in the fringe images, while a CNN branch is designed to collect local details in the speckle images.<n>Our method can reduce fringe order ambiguity while producing high-accuracy results on self-made datasets.
  arXiv  Detail & Related papers  (2024-07-19T10:49:26Z)
• ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection [65.59969454655996]
  We propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. We also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-26T17:46:25Z)
• Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising [54.110544509099526]
  Hyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. We propose a hybrid convolution and attention network (HCANet) to enhance HSI denoising. Experimental results on mainstream HSI datasets demonstrate the rationality and effectiveness of the proposed HCANet.
  arXiv  Detail & Related papers  (2024-03-15T07:18:43Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution [13.894645293832044]
  Transformer-based models have shown competitive performance in remote sensing image super-resolution (RSISR) We propose a novel transformer architecture called Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network (SPIFFNet) for RSISR. Our proposed model effectively enhances global cognition and understanding of the entire image, facilitating efficient integration of features cross-stages.
  arXiv  Detail & Related papers  (2023-07-06T13:19:06Z)
• Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution [64.25751738088015]
  Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks. Transformers that need to incorporate contextual information to extract features dynamically are neglected. We propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer.
  arXiv  Detail & Related papers  (2022-07-06T16:32:29Z)
• RSI-Net: Two-Stream Deep Neural Network Integrating GCN and Atrous CNN for Semantic Segmentation of High-resolution Remote Sensing Images [3.468780866037609]
  Two-stream deep neural network for semantic segmentation of remote sensing images (RSI-Net) is proposed in this paper. Experiments are implemented on the Vaihingen, Potsdam and Gaofen RSI datasets. Results demonstrate the superior performance of RSI-Net in terms of overall accuracy, F1 score and kappa coefficient when compared with six state-of-the-art RSI semantic segmentation methods.
  arXiv  Detail & Related papers  (2021-09-19T15:57:20Z)
• Deep Adaptive Inference Networks for Single Image Super-Resolution [72.7304455761067]
  Single image super-resolution (SISR) has witnessed tremendous progress in recent years owing to the deployment of deep convolutional neural networks (CNNs) In this paper, we take a step forward to address this issue by leveraging the adaptive inference networks for deep SISR (AdaDSR) Our AdaDSR involves an SISR model as backbone and a lightweight adapter module which takes image features and resource constraint as input and predicts a map of local network depth.
  arXiv  Detail & Related papers  (2020-04-08T10:08:20Z)
• Resolution Adaptive Networks for Efficient Inference [53.04907454606711]
  We propose a novel Resolution Adaptive Network (RANet), which is inspired by the intuition that low-resolution representations are sufficient for classifying "easy" inputs. In RANet, the input images are first routed to a lightweight sub-network that efficiently extracts low-resolution representations. High-resolution paths in the network maintain the capability to recognize the "hard" samples.
  arXiv  Detail & Related papers  (2020-03-16T16:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.