Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution

• URL: http://arxiv.org/abs/2107.06536v1
• Date: Wed, 14 Jul 2021 08:06:19 GMT
• Title: Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution
• Authors: Meng Xu, Zhihao Wang, Jiasong Zhu, Xiuping Jia, Sen Jia
• Abstract summary: Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost. We propose a network based on the generative adversarial network (GAN) to generate high resolution remote sensing images.
• Score: 17.04588012373861
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost, thereby providing a feasible way to acquire high-resolution remote sensing images, which are difficult to obtain due to the high cost of acquisition equipment and complex weather. Clearly, image super-resolution is a severe ill-posed problem. Fortunately, with the development of deep learning, the powerful fitting ability of deep neural networks has solved this problem to some extent. In this paper, we propose a network based on the generative adversarial network (GAN) to generate high resolution remote sensing images, named the multi-attention generative adversarial network (MA-GAN). We first designed a GAN-based framework for the image SR task. The core to accomplishing the SR task is the image generator with post-upsampling that we designed. The main body of the generator contains two blocks; one is the pyramidal convolution in the residual-dense block (PCRDB), and the other is the attention-based upsample (AUP) block. The attentioned pyramidal convolution (AttPConv) in the PCRDB block is a module that combines multi-scale convolution and channel attention to automatically learn and adjust the scaling of the residuals for better results. The AUP block is a module that combines pixel attention (PA) to perform arbitrary multiples of upsampling. These two blocks work together to help generate better quality images. For the loss function, we design a loss function based on pixel loss and introduce both adversarial loss and feature loss to guide the generator learning. We have compared our method with several state-of-the-art methods on a remote sensing scene image dataset, and the experimental results consistently demonstrate the effectiveness of the proposed MA-GAN.

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