Weather Radar Super-Resolution Reconstruction Based on Residual Attention Back-Projection Network

Qiu Yu, Ming Qiao Zhu, Qiangyu Zeng, Hao Wang, Qingqing Chen, Xiangyu Fu, Zhipeng Qing

Remote Sensing · 10 April 2023 · China Meteorological Administration (CMA)

In one sentence. Residual attention back-projection network super-resolves weather radar base echoes, outperforming compared CNN methods visually and quantitatively in echo detail and edges.

Abstract

Convolutional neural networks (CNNs) have been utilized extensively to improve the resolution of weather radar. Most existing CNN-based super-resolution algorithms using PPI (Plan position indicator, which provides a maplike presentation in polar coordinates of range and angle) images plotted by radar data lead to the loss of some valid information by using image processing methods for super-resolution reconstruction. To solve this problem, a weather radar that echoes the super-resolution reconstruction algorithm—based on residual attention back-projection network (RABPN)—is proposed to improve the the radar base data resolution. RABPN consists of multiple Residual Attention Groups (RAGs) connected with long skip connections to form a deep network; each RAG is composed of some residual attention blocks (RABs) connected with short skip connections. The residual attention block mined the mutual relationship between low-resolution radar echoes and high-resolution radar echoes by adding a channel attention mechanism to the deep back-projection network (DBPN). Experimental results demonstrate that RABPN outperforms the algorithms compared in this paper in visual evaluation aspects and quantitative analysis, allowing a more refined radar echo structure, especially in terms of echo details and edge structure features.

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