Sampling method for fire point data based on multi-source remote sensing data fusion

Xiaohong Zhu; Qi Wang

doi:10.1117/12.3048883

27 November 2024 Sampling method for fire point data based on multi-source remote sensing data fusion

Xiaohong Zhu, Qi Wang

Proceedings Volume 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024); 134020N (2024) https://doi.org/10.1117/12.3048883
Event: International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 2024, Zhengzhou, China

Abstract

Utilizing deep learning for fire point detection is currently one of the most popular approaches in satellite remote sensing. However, even with appropriate models, the variable quality of fire point training data can significantly impact detection accuracy. Regarding this issue, the study introduces a sampling method for fire point data that combines VIIRS fire point product J1 with high spatial resolution Sentinel-2 data. It enhances the OTSU algorithm using Sentinel-2 data features to identify burned areas and performs spatio-temporal matching with fire point product J1, classifying and sampling through post-validation methods. The sampling method was validated and evaluated using fire point verification data obtained through on-site manual verification and visual interpretation. The results demonstrate that the proposed method can more accurately and efficiently sample true and false fire point data, generate a large number of high-quality samples, and provide data support for deep learning fire point detection models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaohong Zhu and Qi Wang "Sampling method for fire point data based on multi-source remote sensing data fusion", Proc. SPIE 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 134020N (27 November 2024); https://doi.org/10.1117/12.3048883

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KEYWORDS

Fire

Data modeling

Combustion

Remote sensing

Satellites

Statistical modeling

Deep learning

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