Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification

Hnatushenko, Viktoriia; Hnatushenko, Volodymyr; Soldatenko, Dmytro

doi:https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-223-2024

Articles | Volume XLVIII-3-2024

https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-223-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-223-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume XLVIII-3-2024

07 Nov 2024

| 07 Nov 2024

Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification

Viktoriia Hnatushenko, Volodymyr Hnatushenko, and Dmytro Soldatenko

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ENHANCING THE QUALITY OF CNN-BASED BURNED AREA DETECTION IN SATELLITE IMAGERY THROUGH DATA AUGMENTATION

V. Hnatushenko, V. Hnatushenko, D. Soldatenko, and C. Heipke

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1749–1755, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1749-2023,https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1749-2023, 2023

The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.

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