Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using ...

In this paper, we proposed an approach for super-resolution land cover mapping on remote sensing images based on the deep learning technique, ...

Super-resolution mapping (SRM) is a technique to estimate a fine spatial resolution land cover map from coarse spatial resolution fractional proportion images.

Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using ...

In this paper, we propose an approach for super-resolution land cover mapping on remote sensing images based on a Convolutional Neural Network (CNN).

Aug 4, 2019 · Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images.

Abstract: Super-resolution mapping (SRM) is a technique to estimate a fine spatial resolution land cover map from coarse spatial resolution fractional ...

An approach for super-resolution land cover mapping on remote sensing images based on the deep learning technique, namely Convolutional Neural Network (CNN) ...

A deep convolutional neural network was first trained to estimate a fine resolution indicator image for each class from the coarse resolution fractional image, ...

Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using ...