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MANet: A Network Architecture for Remote Sensing Spatiotemporal Fusion Based on Multiscale and Attention Mechanisms

Obtaining high-spatial–high-temporal (HTHS) resolution remote sensing images from a single sensor remains a great challenge due to the cost and technical limitations. Spatiotemporal fusion (STF) technology breaks through the technical limitations of existing sensors and provides a convenient and economical solution for obtaining HTHS resolution images. At present, most STF methods use stacked convolutional layers to extract image features and then obtain fusion images by using a summation strategy. However, these convolution operations may lead to the loss of feature information, and the summation strategy results in poorly fused images due to a lack of consideration of global spatial feature information. To address these issues, this article proposes a STF network architecture based on multiscale and attention mechanisms (MANet). The multiscale mechanism module composed of dilated convolutions is used to extract the detailed features of low-spatial resolution remote sensing images at multiple scales. The channel attention mechanism adaptively adjusts the weights of the feature map channels to retain more temporal and spatial information in the upsampling process, while the non-local attention mechanism adjusts the initial fusion images to obtain more accurate predicted images by calculating the correlation between pixels. We use two datasets with different characteristics to conduct the experiments, and the results prove that the proposed MANet method with fewer parameters obtains better fusion results than the existing machine learning-based and deep learning-based fusion methods.