Hierarchical Feature Aggregation and Self-Learning Network for Remote Sensing Image Continuous-Scale Super-Resolution

Abstract

Conducting research on remote sensing image (RSI) super-resolution (SR) is important, especially in terms of the continuous scale, which is beneficial to the application of RSI, such as RSI object detection and data fusion. Continuous-scale SR aims to use a single model to achieve SR at arbitrary (integer and noninteger) scale factors. Therefore, in this letter, we propose a hierarchical feature aggregation and self-learning network for RSI continuous-scale SR (RSI-HFAS). Our network can magnify the RSI continuously, which is beneficial for extracting the RSI multiscale features. First, we design a hierarchical feature aggregation module (HFAM) that is used for hierarchical feature extraction by placing convolutional layers on different floors and completing global feature fusion, which is crucial for achieving RSI continuous-scale SR with a single model. Second, the proposed network introduces a feedback mechanism, which can refine the hierarchical feature through feature feedback and enrich the texture parts of the RSI step by step. Finally, we design a self-learning upscaling structure to dynamically predict the number and weights of the upsampling filters, which can achieve RSI continuous-scale SR. Compared to the meta-learning based on enhanced deep SR (META-EDSR) method, our experimental results show a nearly 0.2-dB improvement on the metrics of the peak signal-to-noise ratio (PSNR).