Awesome

Implementation of the methods described in the paper entitled “Multi-task deep learning for satellite image pansharpening and segmentation”

Multi-task Framework Architecture

Framework

Software Architecture

Software Architecture

Dependencies

Python 3.6.4
Tensorflow 1.10.0
Numpy 1.14.2
GDAL 2.2.4
The codes have been tested on Fedora 25

Visualization (Optional)

We recommend to use QGIS, where the outputs can easily be displayed despite of the image size.

Usage

The solver sub-directories, namely training_solvers and test_solvers contain solvers, which train a model and test the trained model (See the figure under Software Architecture section).
To train a model, enter the following command (we assume that you are under multi-task directory, otherwise you will get an error): python3 train_solvers/train_solver<id>.py
To test a trained model, enter this command: python3 test_solvers/test_solver<id>.py

<id> in the commands above determines which solver to run.

Example Visual Results From the World-View3 Dataset

Here, we illustrate several original visual outputs from the World-View3 dataset for different methods including our multi-task framework.

Results

Citation

@inproceedings{khalel2019multi,
  title={Multi-task deep learning for satellite image pansharpening and segmentation},
  author={Khalel, Andrew and Tasar, Onur and Charpiat, Guillaume and Tarabalka, Yuliya},
  booktitle={IEEE International Geoscience and Remote Sensing Symposium--IGARSS 2019},
  year={2019}
}