当前位置: X-MOL 学术J. Appl. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
SCRNet: an efficient spatial channel attention residual network for spatiotemporal fusion
Journal of Applied Remote Sensing ( IF 1.4 ) Pub Date : 2022-08-01 , DOI: 10.1117/1.jrs.16.036512
Dajiang Lei 1 , Zhiqiang Huang 1 , Liping Zhang 1 , Weisheng Li 1
Affiliation  

Spatiotemporal fusion is a simple, feasible, and economical method for balancing the temporal and spatial resolutions of satellite images. However, the current spatiotemporal fusion methods have some disadvantages, such as their insufficient feature extraction abilities and unsatisfactory fusion image effects. To obtain higher-quality spatiotemporal fusion images, a spatiotemporal data fusion method based on deep learning is proposed. This method combines an attention mechanism and a residual learning strategy to design an asymmetric spatial channel attention residual network (SCRNet). For different input images, the SCRNet extracts rich feature information to fuse high-quality images in a more emphatic and scientific manner than other approaches. Specifically, we design an efficient and flexible spatial channel attention mechanism that not only focuses on spatial information but also takes channel information into account. The optimized residual network can further enhance the ability of the network to extract feature information. In addition, we design a compound loss function and propose an edge loss to further enrich the extracted feature information and improve the resulting image quality. Experimental results on two datasets show that the proposed method outperforms existing fusion methods in subjective and objective evaluations.

中文翻译:

SCRNet:一种用于时空融合的高效空间通道注意力残差网络

时空融合是一种简单、可行、经济的平衡卫星图像时空分辨率的方法。然而,目前的时空融合方法存在特征提取能力不足、融合图像效果不理想等不足。为了获得更高质量的时空融合图像,提出了一种基于深度学习的时空数据融合方法。该方法结合了注意力机制和残差学习策略,设计了一个非对称空间通道注意力残差网络(SCRNet)。对于不同的输入图像,SCRNet提取丰富的特征信息,以比其他方法更强调和科学的方式融合高质量的图像。具体来说,我们设计了一种高效灵活的空间通道注意机制,不仅关注空间信息,还考虑了通道信息。优化后的残差网络可以进一步增强网络提取特征信息的能力。此外,我们设计了一种复合损失函数,并提出了一种边缘损失,以进一步丰富提取的特征信息,提高图像质量。在两个数据集上的实验结果表明,所提出的方法在主观和客观评价方面优于现有的融合方法。我们设计了一个复合损失函数并提出了一种边缘损失,以进一步丰富提取的特征信息并提高最终的图像质量。在两个数据集上的实验结果表明,所提出的方法在主观和客观评价方面优于现有的融合方法。我们设计了一个复合损失函数并提出了一种边缘损失,以进一步丰富提取的特征信息并提高最终的图像质量。在两个数据集上的实验结果表明,所提出的方法在主观和客观评价方面优于现有的融合方法。
更新日期:2022-08-01
down
wechat
bug