Graph_Learning_Based_on_Signal_Smoothness_Representation_for_Change_Detection

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Graph_Learning_Based_on_Signal_Smoothness_Representation_for_Change_Detection

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Graph signal processing for change detection

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# Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection

This repository is a proposed approach based on Graph Signal processing for change detection under the folllowing name:

* "[Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection](http://dx.doi.org/10.1109/TGRS.2022.3168126)" published in IEEE Transactions on Geoscience and Remote Sensing (TGRS).

## Datasets

------------Available datasets and Parameters------------

Dataset | Vx = Vy | K | Alpha |
   --------|:-------:|:-:|:-----:|
   Mulargia_dataset    |      11       |      1464     |   0.1     |
   Omodeo_dataset      |      20       |      174      |   0.109   |
   Alaska_dataset      |      11       |      1479     |   0.013   |
   Madeirinha_dataset  |      10       |      410      |   0.2     |
   Canada_dataset      |      44       |      2400     |   0.1     |
   Gloucester_1_dataset|      144      |      171      |   0.1     |
   Katios_dataset      |      23       |      102      |   0.738   |
   Atlantico_dataset   |      17       |      2029     |   0.103   |
   SF_dataset          |      16       |      260      |   0.215   |
   Wenchuan_dataset    |      8        |      1740     |   0.5     |
   Toulouse_dataset    |      86       |      192      |   0.002   |
   California_dataset  |      73       |      291      |   0.1     |
   Bastrop_dataset     |      23       |      393      |   0.1     |
   Gloucester_2_dataset|      150      |      444      |   0.042   |

These datasets are part of the following works:

* "[Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection](http://dx.doi.org/10.1109/TGRS.2022.3168126)" published in IEEE Transactions on Geoscience and Remote Sensing (TGRS).

* "[Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops](https://www.mdpi.com/2072-4292/12/17/2683)" published in MDPI Remote Sensing.

Please if you use the datasets and the codes cite our works as:

@article{&nbsp;&nbsp;&nbsp;JimenezSierra2022graph, 
&nbsp;&nbsp;&nbsp;author={Jimenez-Sierra, David Alejandro and Quintero-Olaya, David Alfredo and Alvear-Mu{\\~n}oz, Juan Carlos and Ben{\\'i}tez-Restrepo, Hern{\\'a}n Dar{\\'i}o and Florez-Ospina, Juan Felipe and Chanussot, Jocelyn}, 
&nbsp;&nbsp;&nbsp;journal={IEEE Transactions on Geoscience and Remote Sensing}, 
&nbsp;&nbsp;&nbsp;title={Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection}, 
&nbsp;&nbsp;&nbsp;year={2022}, 
&nbsp;&nbsp;&nbsp;volume={60}, 
&nbsp;&nbsp;&nbsp;number={}, 
&nbsp;&nbsp;&nbsp;pages={1-16}, 
&nbsp;&nbsp;&nbsp;doi={10.1109/TGRS.2022.3168126} 
}

@article{&nbsp;&nbsp;&nbsp;JimenezSierra2020graph, 
 &nbsp;&nbsp;&nbsp;title={Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops}, 
 &nbsp;&nbsp;&nbsp;author={Jimenez-Sierra, David Alejandro and Ben{\\'i}tez-Restrepo, Hern{\\'a}n Dar{\\'i}o and Vargas-Cardona, Hern{\\'a}n Dar{\\'i}o and Chanussot, Jocelyn}, 
 &nbsp;&nbsp;&nbsp;journal={Remote Sensing}, 
 &nbsp;&nbsp;&nbsp;volume={12}, 
 &nbsp;&nbsp;&nbsp;number={17}, 
 &nbsp;&nbsp;&nbsp;pages={2683}, 
 &nbsp;&nbsp;&nbsp;year={2020}, 
 &nbsp;&nbsp;&nbsp;publisher={Multidisciplinary Digital Publishing Institute} 
 }

## Requirements

In order to run the code, you will need to download and add to the path the following toolboxes:

* GMMSP [toolbox](https://github.com/ahban/GMMSP-superpixel) for superpixel segmentation 
* Graph signal processing [toolbox](https://epfl-lts2.github.io/gspbox-html) for graph smoothness prior. 
* Unlocbox [toolbox](https://github.com/epfl-lts2/unlocbox) for graph learning optimization.

### Canada and California datasets

Please read the data [readme](https://github.com/DavidJimenezS/Graph_Learning_Based_on_Signal_Smoothness_Representation_for_Change_Detection/tree/main/Data) file to download these datasets.

## PYTHON VERSION AVAILABLE
Now you can have access to the Python version of this code in the Juan Florez-Ospina [Github](https://github.com/jfflorez/Graph-based-methods-for-change-detection-in-remote-sensing-images)

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