A Remote-Sensing Image Pan-Sharpening Method Based on Multi-Scale Channel Attention Residual Network

IEEE Access

Xin Li¹  Xin Lyu¹  Feng Xu¹  Shengyang Li²  Daofang Liu²  Yao Tong³  Ziqi Chen⁴ 

[1] College of Computer and Information, Hohai University, Nanjing, China;Information Center, Yellow River Conservancy Commission, Zhengzhou, China;School of Information Engineering, Zhengzhou University, Zhengzhou, China;School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China;

关键词: Pan-sharpening;    deep learning;    multi-scale feature extraction;    multi-residual learning;    channel-attention mechanism;    convergence acceleration;   

DOI  :  10.1109/ACCESS.2020.2971502

来源: DOAJ

【 摘 要 】

Pan-sharpening is a significant task that aims to generate high spectral- and spatial- resolution remote-sensing image by fusing multi-spectral (MS) and panchromatic (PAN) image. The conventional approaches are insufficient to protect the fidelity both in spectral and spatial domains. Inspired by the robust capability and outstanding performance of convolutional neural networks (CNN) in natural image super-resolution tasks, CNN-based pan-sharpening methods are worthy of further exploration. In this paper, a novel pan-sharpening method is proposed by introducing a multi-scale channel attention residual network (MSCARN), which can represent features accurately and reconstruct a pan-sharpened image comprehensively. In MSCARN, the multi-scale feature extraction blocks comprehensively extract the coarse structures and high-frequency details. Moreover, the multi-residual architecture guarantees the consistency of feature learning procedure and accelerates convergence. Specifically, we introduce a channel attention mechanism to recalibrate the channel-wise features by considering interdependencies among channels adaptively. The extensive experiments are implemented on two real-datasets from GaoFen series satellites. And the results show that the proposed method performs better than the existing methods both in full-reference and no-reference metrics, meanwhile, the visual inspection displays in accordance with the quantitative metrics. Besides, in comparison with pan-sharpening by convolutional neural networks (PNN), the proposed method achieves faster convergence rate and lower loss.

【 授权许可】

Unknown