Abstract:

Objects in remote sensing images are small in size, unevenly distributed, and highly coupled. In view of the current situation that the feature extraction information of remote sensing image super-resolution models is single and underutilized, this study proposes a multi-path parallel multi-scale feature reuse network model to improve performance of image reconstruction. The model fuses feature information extracted from multiple network residual blocks using a structure of local feature cascade and global feature fusion, where each residual block is serially connected by two multi-scale convolutional units. The multi-scale convolution units construct multiple branches to extract image features in parallel through cross-fusion of feature information. At the same time, short skip connections are introduced to strengthen feature reuse between different branches, and long skip connections are introduced to strengthen feature fusion at different depths of the network. When the amplification factor is 4, the peak signal-to-noise ratios of the model on the two test sets are 29.653 1 dB and 29.037 4 dB respectively, and they are significantly improved compared with the test results of other models. Therefore, the proposed model has a good effect on super-resolution reconstruction of remote sensing images.

Key words: remote sensing image, super-resolution, multi-path, parallel extraction, multi-scale, feature reuse, skip connection, convolutional neural network