Details
| Original language | English |
|---|---|
| Pages (from-to) | 1749-1755 |
| Number of pages | 7 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 48 |
| Issue number | 1 |
| Publication status | Published - 14 Dec 2023 |
| Event | ISPRS Geospatial Week 2023 - Kairo, Egypt Duration: 2 Sept 2023 → 7 Sept 2023 |
Abstract
This study aims to enhance the quality of detecting burned areas in satellite imagery using deep learning by optimizing the training dataset volume through the application of various augmentation methods. The study analyzes the impact of image flipping, rotation, and noise addition on the overall accuracy for different classes of burned areas in a forest: fire, burned, smoke and background. Results demonstrate that while single augmentation techniques such as flipping and rotation alone did not result in significant improvements, a combined approach and the addition of noise resulted in an enhancement of the classification accuracy. Moreover, the study shows that augmenting the dataset through the use of multiple augmentation methods concurrently, resulting in a fivefold increase in input data, also enhanced the recognition accuracy. The study also highlights the need for further research in developing more efficient CNN models and in experimenting with additional augmentation methods to improve the accuracy of burned area detection, which would benefit environmental protection and emergency response services.
Keywords
- Augmentation, CNN, Forest Fire, Satellite Images
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 48, No. 1, 14.12.2023, p. 1749-1755.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Enhancing The Quality Of CNN-Based Burned Area Detection In Satellite Imagery Through Data Augmentation
AU - Hnatushenko, Vik
AU - Hnatushenko, V.
AU - Soldatenko, D.
AU - Heipke, C.
PY - 2023/12/14
Y1 - 2023/12/14
N2 - This study aims to enhance the quality of detecting burned areas in satellite imagery using deep learning by optimizing the training dataset volume through the application of various augmentation methods. The study analyzes the impact of image flipping, rotation, and noise addition on the overall accuracy for different classes of burned areas in a forest: fire, burned, smoke and background. Results demonstrate that while single augmentation techniques such as flipping and rotation alone did not result in significant improvements, a combined approach and the addition of noise resulted in an enhancement of the classification accuracy. Moreover, the study shows that augmenting the dataset through the use of multiple augmentation methods concurrently, resulting in a fivefold increase in input data, also enhanced the recognition accuracy. The study also highlights the need for further research in developing more efficient CNN models and in experimenting with additional augmentation methods to improve the accuracy of burned area detection, which would benefit environmental protection and emergency response services.
AB - This study aims to enhance the quality of detecting burned areas in satellite imagery using deep learning by optimizing the training dataset volume through the application of various augmentation methods. The study analyzes the impact of image flipping, rotation, and noise addition on the overall accuracy for different classes of burned areas in a forest: fire, burned, smoke and background. Results demonstrate that while single augmentation techniques such as flipping and rotation alone did not result in significant improvements, a combined approach and the addition of noise resulted in an enhancement of the classification accuracy. Moreover, the study shows that augmenting the dataset through the use of multiple augmentation methods concurrently, resulting in a fivefold increase in input data, also enhanced the recognition accuracy. The study also highlights the need for further research in developing more efficient CNN models and in experimenting with additional augmentation methods to improve the accuracy of burned area detection, which would benefit environmental protection and emergency response services.
KW - Augmentation
KW - CNN
KW - Forest Fire
KW - Satellite Images
UR - http://www.scopus.com/inward/record.url?scp=85183302615&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLVIII-1-W2-2023-1749-2023
DO - 10.5194/isprs-archives-XLVIII-1-W2-2023-1749-2023
M3 - Conference article
AN - SCOPUS:85183302615
VL - 48
SP - 1749
EP - 1755
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - 1
T2 - ISPRS Geospatial Week 2023
Y2 - 2 September 2023 through 7 September 2023
ER -