Jun 7, 2023 · It contains two kinds of convolutional attention-based single-scale feature extraction modules (SEM) to explore the global spatial context ...

Super-resolution (SR) neural networks have recently achieved great progress in restoring high-quality remote sensing images (RSIs) at low zoom-in magnitudes.

In this paper, we present a new Hybrid Attention-based U-shaped Network (HAUNet) for RSISR to effectively explore the multi-scale features and enhance the ...

Our design can effectively extract and adaptively aggregate multi-scale information from local to global, which helps to improve super-resolution results and.

Hybrid Attention-Based U-Shaped Network for Remote Sensing Image Super-Resolution · List of references · Publications that cite this publication.

The proposed Deep RegNet-150 includes up-sampling and down-sampling portions with Residual Channel Attention Block (RCAB) for SISR.

Remote-Sensing-Image-Super-Resolution. Here I mainly list the deep learning-based remote sensing image super-resolution methods.

An attention-based multilevel feature fusion network (AMFFN) to enhance the resolution of RSIs and is expanded to the field of natural image ...

Feb 3, 2024 · This paper introduces a hybrid U-shaped and transformer network for change detection in high-resolution remote sensing images.

Apr 11, 2024 · This paper introduces a hybrid U‐shaped and transformer network for change detection in high‐resolution remote sensing images. Specifically ...