Abstract
In recent years, convolutional neural networks (CNNs) have obtained promising results in single-image super-resolution (SR) for remote sensing images. However, most existing methods are inadequate for remote sensing image SR due to the high computational cost required. Therefore, enhancing the representation ability with fewer parameters and a shorter prediction time is a challenging and critical task for remote sensing image SR. In this paper, we propose a novel CNN called a parallel-connected residual channel attention network (PCRCAN). Specifically, inspired by group convolution, we propose a parallel module with feature aggregation modules in PCRCAN. The parallel module significantly reduces the model parameters and fully integrates feature maps by widening the network architecture. In addition, to reduce the difficulty of training a complex deep network and improve model performance, we use a residual channel attention block as the basic feature mapping unit instead of a single convolutional layer. Experiments on a public remote sensing dataset UC Merced land-use dataset revealed that PCRCAN achieved higher accuracy, efficiency, and visual improvement than most state-of-the-art methods.
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Acknowledgement
This work is supported in part by Japan Society for Promotion of Science (JSPS) under Grant No. 19J13820, the Grant-in-Aid for Young Scientists (18K18078), Grant-in-Aid for Scientific Research (B) (18H03267) and Grant-in-Aid for Challenging Research (Exploratory) (20K21821).
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Li, Y., Iwamoto, Y., Lin, L., Chen, YW. (2021). Parallel-Connected Residual Channel Attention Network for Remote Sensing Image Super-Resolution. In: Sato, I., Han, B. (eds) Computer Vision – ACCV 2020 Workshops. ACCV 2020. Lecture Notes in Computer Science(), vol 12628. Springer, Cham. https://doi.org/10.1007/978-3-030-69756-3_2
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