19 papers with code • 10 benchmarks • 4 datasets
As a remote sensing image processing task, Pan-sharpening aims to increase the spatial resolution of the low-resolution multispectral image with the guidance of the corresponding panchromatic image.
A further problem is the scarcity of training data, which causes a limited generalization ability and a poor performance on off-training test images.
Pansharpening aims to fuse a multispectral (MS) image with an associated panchromatic (PAN) image, producing a composite image with the spectral resolution of the former and the spatial resolution of the latter.
Convolutional Neural Networks (CNN)-based approaches have shown promising results in pansharpening of satellite images in recent years.
The proposed network is composed of an encoder-decoder network that exploits multi-scale features of a guidance image and a deep decoder network that generates an output image.
Retaining spatial characteristics of panchromatic image and spectral information of multispectral bands is a critical issue in pansharpening.
The required pre-processing steps have been defined and 13 pansharpening methods have been applied and evaluated for their ability to spectrally discriminate plastics from water.
Sentinel-2 Sharpening Using a Single Unsupervised Convolutional Neural Network With MTF-Based Degradation Model
However, the downside of those methods is that the DL-based methods need to be trained separately for the 20 m and the 60 m bands in a supervised manner at reduced resolution, while the model-based methods heavily depend on the hand-crafted image priors.
To estimate the PAN image of the up-sampled HSI, we also propose a learnable spectral response function (SRF).