Change Detection for Forest Ecosystems Using Remote Sensing Images with Siamese Attention U-Net
Forest ecosystems are critical components of Earth’s biodiversity and play vital roles in climate regulation and carbon sequestration. They face increasing threats from deforestation, wildfires, and other anthropogenic activities. Timely detection and monitoring of changes in forest landscapes pose...
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MDPI AG
2024-09-01
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| author | Ashen Iranga Hewarathna Luke Hamlin Joseph Charles Palanisamy Vigneshwaran Romiyal George Selvarajah Thuseethan Chathrie Wimalasooriya Bharanidharan Shanmugam |
| author_facet | Ashen Iranga Hewarathna Luke Hamlin Joseph Charles Palanisamy Vigneshwaran Romiyal George Selvarajah Thuseethan Chathrie Wimalasooriya Bharanidharan Shanmugam |
| author_sort | Ashen Iranga Hewarathna |
| collection | DOAJ |
| description | Forest ecosystems are critical components of Earth’s biodiversity and play vital roles in climate regulation and carbon sequestration. They face increasing threats from deforestation, wildfires, and other anthropogenic activities. Timely detection and monitoring of changes in forest landscapes pose significant challenges for government agencies. To address these challenges, we propose a novel pipeline by refining the U-Net design, including employing two different schemata of early fusion networks and a Siam network architecture capable of processing RGB images specifically designed to identify high-risk areas in forest ecosystems through change detection across different time frames in the same location. It annotates ground truth change maps in such time frames using an encoder–decoder approach with the help of an enhanced feature learning and attention mechanism. Our proposed pipeline, integrated with ResNeSt blocks and SE attention techniques, achieved impressive results in our newly created forest cover change dataset. The evaluation metrics reveal a Dice score of 39.03%, a kappa score of 35.13%, an F1-score of 42.84%, and an overall accuracy of 94.37%. Notably, our approach significantly outperformed multitasking model approaches in the ONERA dataset, boasting a precision of 53.32%, a Dice score of 59.97%, and an overall accuracy of 97.82%. Furthermore, it surpassed multitasking models in the HRSCD dataset, even without utilizing land cover maps, achieving a Dice score of 44.62%, a kappa score of 11.97%, and an overall accuracy of 98.44%. Although the proposed model had a lower F1-score than other methods, other performance metrics highlight its effectiveness in timely detection and forest landscape monitoring, advancing deep learning techniques in this field. |
| format | Article |
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| issn | 2227-7080 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Technologies |
| spelling | doaj-art-72fd5dbd67c3490f91f14031d94bfd442025-08-20T01:55:52ZengMDPI AGTechnologies2227-70802024-09-0112916010.3390/technologies12090160Change Detection for Forest Ecosystems Using Remote Sensing Images with Siamese Attention U-NetAshen Iranga Hewarathna0Luke Hamlin1Joseph Charles2Palanisamy Vigneshwaran3Romiyal George4Selvarajah Thuseethan5Chathrie Wimalasooriya6Bharanidharan Shanmugam7Faculty of Computing, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri LankaEnergy and Resource Institute, Faculty of Science and Technology, Charles Darwin University, Darwin, NT 0810, AustraliaFaculty of Engineering, Friedrich-Alexander-University (FAU), 91054 Erlangen, GermanyFaculty of Computing, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri LankaDepartment of Computer Engineering, University of Peradeniya, Peradeniya 20400, Sri LankaEnergy and Resource Institute, Faculty of Science and Technology, Charles Darwin University, Darwin, NT 0810, AustraliaSchool of Computing, University of Otago, Dunedin 9016, New ZealandEnergy and Resource Institute, Faculty of Science and Technology, Charles Darwin University, Darwin, NT 0810, AustraliaForest ecosystems are critical components of Earth’s biodiversity and play vital roles in climate regulation and carbon sequestration. They face increasing threats from deforestation, wildfires, and other anthropogenic activities. Timely detection and monitoring of changes in forest landscapes pose significant challenges for government agencies. To address these challenges, we propose a novel pipeline by refining the U-Net design, including employing two different schemata of early fusion networks and a Siam network architecture capable of processing RGB images specifically designed to identify high-risk areas in forest ecosystems through change detection across different time frames in the same location. It annotates ground truth change maps in such time frames using an encoder–decoder approach with the help of an enhanced feature learning and attention mechanism. Our proposed pipeline, integrated with ResNeSt blocks and SE attention techniques, achieved impressive results in our newly created forest cover change dataset. The evaluation metrics reveal a Dice score of 39.03%, a kappa score of 35.13%, an F1-score of 42.84%, and an overall accuracy of 94.37%. Notably, our approach significantly outperformed multitasking model approaches in the ONERA dataset, boasting a precision of 53.32%, a Dice score of 59.97%, and an overall accuracy of 97.82%. Furthermore, it surpassed multitasking models in the HRSCD dataset, even without utilizing land cover maps, achieving a Dice score of 44.62%, a kappa score of 11.97%, and an overall accuracy of 98.44%. Although the proposed model had a lower F1-score than other methods, other performance metrics highlight its effectiveness in timely detection and forest landscape monitoring, advancing deep learning techniques in this field.https://www.mdpi.com/2227-7080/12/9/160deforestationchange detectionSiamese attention mechanismU-Net |
| spellingShingle | Ashen Iranga Hewarathna Luke Hamlin Joseph Charles Palanisamy Vigneshwaran Romiyal George Selvarajah Thuseethan Chathrie Wimalasooriya Bharanidharan Shanmugam Change Detection for Forest Ecosystems Using Remote Sensing Images with Siamese Attention U-Net Technologies deforestation change detection Siamese attention mechanism U-Net |
| title | Change Detection for Forest Ecosystems Using Remote Sensing Images with Siamese Attention U-Net |
| title_full | Change Detection for Forest Ecosystems Using Remote Sensing Images with Siamese Attention U-Net |
| title_fullStr | Change Detection for Forest Ecosystems Using Remote Sensing Images with Siamese Attention U-Net |
| title_full_unstemmed | Change Detection for Forest Ecosystems Using Remote Sensing Images with Siamese Attention U-Net |
| title_short | Change Detection for Forest Ecosystems Using Remote Sensing Images with Siamese Attention U-Net |
| title_sort | change detection for forest ecosystems using remote sensing images with siamese attention u net |
| topic | deforestation change detection Siamese attention mechanism U-Net |
| url | https://www.mdpi.com/2227-7080/12/9/160 |
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