Parallel-Connected Residual Channel Attention Network for Remote Sensing Image Super-Resolution

Yinhao Li, Yutaro Iwamoto, Lanfen Lin, Yen-Wei Chen; Proceedings of the Asian Conference on Computer Vision (ACCV) Workshops, 2020

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.