基于双重注意力机制的遥感图像时空融合方法

Spatiotemporal Fusion Method for Remote Sensing Images Based on Dual Attention Mechanisms

由于遥感成像技术条件的限制,难以同时获得既有高时间分辨率又具有高空间分辨率序列的遥感影像,通过时空融合技术可以生成同时具有高时间和高空间分辨率的遥感图像。近年来,时空融合方法层出不穷,这些方法效果良好,但在特征提取有效信息方面仍有不足。针对此问题,提出了一种基于双重注意力机制改进的深度学习模型(ADCSTFN),使得模型在全局的保留和细节场景的重建能力都得到了提高。在实验中,采用Landsat和MODIS数据为研究对象,使用两个开源数据集和一个本地数据集对该方法进行测试,并与4种常用的时空融合方法进行比较。实验结果表明,文中提出的残差网络和双重注意力机制方法能更好地提取图像的有效信息,使用深监督损失函数缓解反向传播时梯度消失的问题,优化了学习过程,使得融合后的效果有了明显的提高。

Due to the limitations of remote sensing imaging technology conditions,it is difficult to obtain sequences of remote sensing images with both high temporal and high spatial resolution simultaneously.However,spatiotemporal fusion techniques can generate remote sensing images with high temporal and high spatial resolution.In recent years,various spatiotemporal fusion methods have emerged,showing good performance but still falling short in effectively extracting meaningful information in feature extraction.A deep learning model based on dual attention mechanism improvement(ADCSTFN)is proposed to address this issue,which enhances the model's ability to preserve global information and reconstruct detailed scenes.In the experiments,Landsat and MODIS data were used as the research subjects,and the proposed method was tested using two open-source datasets and a local dataset,and compared with four commonly used spatiotemporal fusion methods.Experimental results show that the resi-dual network and dual attention mechanism proposed in this paper better extract effective information from images.The use of a deep supervision loss function mitigates the issue of vanishing gradients during backpropagation,optimizes the learning process,and significantly improves the fusion results.