Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification
Today's agricultural sector is characterized by an important role of accurate mapping and monitoring of agriculture with the help of satellite imagery, which allows to optimize the use of resources, to plan crop areas and to forecast productivity. Classification of satellite images with unbalan...
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Copernicus Publications
2024-11-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-3-2024/223/2024/isprs-archives-XLVIII-3-2024-223-2024.pdf |
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| author | V. Hnatushenko V. Hnatushenko V. Hnatushenko D. Soldatenko |
| author_facet | V. Hnatushenko V. Hnatushenko V. Hnatushenko D. Soldatenko |
| author_sort | V. Hnatushenko |
| collection | DOAJ |
| description | Today's agricultural sector is characterized by an important role of accurate mapping and monitoring of agriculture with the help of satellite imagery, which allows to optimize the use of resources, to plan crop areas and to forecast productivity. Classification of satellite images with unbalanced sample distribution is a critical problem in this regard. Traditional machine learning algorithms in particular have limitations in dealing with sample imbalance. In this paper, we proposed convolution neural networks for semantic segmentation, where sample imbalance is considered based on a particular loss function coupled with data augmentation. To illustrate our method, we use Sentinel-2 remote sensing (RS) images covering a number of regions in Ukraine, and then we create an image dataset of the region and for training and testing make data augmentation. The models with different architectural features were investigated.<br />The results demonstrate that the proposed CNN has a higher classification accuracy than the ones discussed in the paper: the classification accuracy on the test dataset reached 96.7% with intersection-over-union values of up to 89.7%. This opens the way for further research in the direction of refining algorithms for classify satellite data with an imbalanced class structure. |
| format | Article |
| id | doaj-art-2f0e0189bfbc40579cf97018ab76407e |
| institution | OA Journals |
| issn | 1682-1750 2194-9034 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-2f0e0189bfbc40579cf97018ab76407e2025-08-20T02:12:41ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342024-11-01XLVIII-3-202422322910.5194/isprs-archives-XLVIII-3-2024-223-2024Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image ClassificationV. Hnatushenko0V. Hnatushenko1V. Hnatushenko2D. Soldatenko3Dept. Information Technologies and Systems, Ukrainian State University of Science and Technologies, Dnipro, UkraineInstitute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, GermanyDept. Information Technologies and Computer Engineering, Dnipro University of Technology, Dnipro, UkraineDept. Information Technologies and Systems, Ukrainian State University of Science and Technologies, Dnipro, UkraineToday's agricultural sector is characterized by an important role of accurate mapping and monitoring of agriculture with the help of satellite imagery, which allows to optimize the use of resources, to plan crop areas and to forecast productivity. Classification of satellite images with unbalanced sample distribution is a critical problem in this regard. Traditional machine learning algorithms in particular have limitations in dealing with sample imbalance. In this paper, we proposed convolution neural networks for semantic segmentation, where sample imbalance is considered based on a particular loss function coupled with data augmentation. To illustrate our method, we use Sentinel-2 remote sensing (RS) images covering a number of regions in Ukraine, and then we create an image dataset of the region and for training and testing make data augmentation. The models with different architectural features were investigated.<br />The results demonstrate that the proposed CNN has a higher classification accuracy than the ones discussed in the paper: the classification accuracy on the test dataset reached 96.7% with intersection-over-union values of up to 89.7%. This opens the way for further research in the direction of refining algorithms for classify satellite data with an imbalanced class structure.https://isprs-archives.copernicus.org/articles/XLVIII-3-2024/223/2024/isprs-archives-XLVIII-3-2024-223-2024.pdf |
| spellingShingle | V. Hnatushenko V. Hnatushenko V. Hnatushenko D. Soldatenko Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification |
| title_full | Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification |
| title_fullStr | Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification |
| title_full_unstemmed | Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification |
| title_short | Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification |
| title_sort | neural network based analysis of forest fire aftermath in class imbalanced remote sensing earth image classification |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-3-2024/223/2024/isprs-archives-XLVIII-3-2024-223-2024.pdf |
| work_keys_str_mv | AT vhnatushenko neuralnetworkbasedanalysisofforestfireaftermathinclassimbalancedremotesensingearthimageclassification AT vhnatushenko neuralnetworkbasedanalysisofforestfireaftermathinclassimbalancedremotesensingearthimageclassification AT vhnatushenko neuralnetworkbasedanalysisofforestfireaftermathinclassimbalancedremotesensingearthimageclassification AT dsoldatenko neuralnetworkbasedanalysisofforestfireaftermathinclassimbalancedremotesensingearthimageclassification |