Deep learning for downscaling remote sensing images: Fusion and super-resolution
The past few years have seen an accelerating integration of deep learning (DL) techniques
into various remote sensing (RS) applications, highlighting their power to adapt and …
into various remote sensing (RS) applications, highlighting their power to adapt and …
Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach
Building footprint is a primary dataset of an urban geographic information system (GIS)
database. Therefore, it is essential to establish a robust and automated framework for large …
database. Therefore, it is essential to establish a robust and automated framework for large …
Transformer-based multistage enhancement for remote sensing image super-resolution
Convolutional neural networks have made a great breakthrough in recent remote sensing
image super-resolution (SR) tasks. Most of these methods adopt upsampling layers at the …
image super-resolution (SR) tasks. Most of these methods adopt upsampling layers at the …
Hybrid-scale self-similarity exploitation for remote sensing image super-resolution
Recently, deep convolutional neural networks (CNNs) have made great progress in remote
sensing image super-resolution (SR). The CNN-based methods can learn powerful feature …
sensing image super-resolution (SR). The CNN-based methods can learn powerful feature …
Continuous remote sensing image super-resolution based on context interaction in implicit function space
Despite its fruitful applications in remote sensing, image super-resolution (SR) is
troublesome to train and deploy as it handles different resolution magnifications with …
troublesome to train and deploy as it handles different resolution magnifications with …
Contextual transformation network for lightweight remote-sensing image super-resolution
Current super-resolution networks typically reduce network parameters and multiadds
operations by designing lightweight structures, but lightening the convolution layer is often …
operations by designing lightweight structures, but lightening the convolution layer is often …
Hybrid attention-based U-shaped network for remote sensing image super-resolution
Recently, remote sensing image super-resolution (RSISR) has drawn considerable attention
and made great breakthroughs based on convolutional neural networks (CNNs). Due to the …
and made great breakthroughs based on convolutional neural networks (CNNs). Due to the …
[HTML][HTML] Super-resolution of sentinel-2 imagery using generative adversarial networks
Sentinel-2 satellites provide multi-spectral optical remote sensing images with four bands at
10 m of spatial resolution. These images, due to the open data distribution policy, are …
10 m of spatial resolution. These images, due to the open data distribution policy, are …
DTCNet: Transformer-CNN distillation for super-resolution of remote sensing image
C Lin, X Mao, C Qiu, L Zou - IEEE Journal of Selected Topics in …, 2024 - ieeexplore.ieee.org
Super-resolution reconstruction technology is a crucial approach to enhance the quality of
remote sensing optical images. Currently, the mainstream reconstruction methods leverage …
remote sensing optical images. Currently, the mainstream reconstruction methods leverage …
Remote sensing image superresolution using deep residual channel attention
The current trend in remote sensing image superresolution (SR) is to use supervised deep
learning models to effectively enhance the spatial resolution of airborne and satellite-based …
learning models to effectively enhance the spatial resolution of airborne and satellite-based …