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Cross-sensor Remote Sensing Imagery Super-resolution via an Edge-guided Attention-based Network by Zhonghang Qiu; Huanfeng Shen; Linwei Yue; Guizhou Zheng is a Environmental Science article available to read on EtoBox.

What is Cross-sensor Remote Sensing Imagery Super-resolution via an Edge-guided Attention-based Network about?

The deep learning based super-resolution (SR) methods have recently achieved remarkable progress in the reconstruction of ideally simulated high-quality remote sensing image datasets. However, due to the large variation in image quality caused by the complex degradation factors, their performance decreases dramatically on real-world images acquired by different satellite sensors. To this end, we propose a cross-sensor SR framework that consists of a cross-sensor degradation modeling strategy for bridging the gap between the images obtained by the source and target sensors, and an edge-guided attention-based SR (EGASR) network to promote the learning of high-frequency feature representation. Specifically, we build a degradation pool on the low-resolution (LR) target sensor to produce a degraded training dataset simulated from the high-resolution (HR) images obtained by the source sensor. Furthermore, the EGASR network, which employs the edge-guided residual attention block (EGRAB) to introduce implicit edge prior to enhance edge-related information, is embedded in the cross-sensor SR framework for reconstructing HR results with sharp details. The proposed method is applied on images

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Author

Zhonghang Qiu; Huanfeng Shen; Linwei Yue; Guizhou Zheng

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)