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<p>In this paper we introduce a new large-scale hyperspectral satellite image dataset named OHID-FF, specifically designed for forest fire detection and classification tasks. The OHID-FF dataset comprises 1,197 hyperspectral images from 22 different scenarios, with each image featuring 32 spectral bands and a spatial resolution of 10 meters per pixel. The dataset covers 22 locations in Australia, encompassing urban areas, mountainous regions, oceans, and other terrains. Compared to existing fire datasets, OHID-FF offers a richer volume of data and higher imaging quality, making it an ideal choice for training deep neural networks. Through benchmark experiments on this dataset, we found that existing methods face challenges in accurately classifying OHID-FF data, setting a new benchmark for hyperspectral imaging classification. Additionally, we provide detailed descriptions of the dataset preparation process, data sources, tile creation, and annotation procedures. Furthermore, we present experimental results using different deep learning models for fire detection and image classification, demonstrating the potential of this dataset in practical applications.</p>

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A Comprehensive Hyperspectral Image Dataset for Forest Fire Detection and Classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月17日

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  • Ashish Mani,
  • Xin Chen,
  • Sergey Gorbachev,
  • Jun Yan,
  • Abhishek Dixit,
  • Yuanyuan Sun,
  • Zhiyu Yan,
  • Jiaqi Wu,
  • Jianwen Deng,
  • Xiaohua Jiang,
  • Yuwei Chen

摘要

In this paper we introduce a new large-scale hyperspectral satellite image dataset named OHID-FF, specifically designed for forest fire detection and classification tasks. The OHID-FF dataset comprises 1,197 hyperspectral images from 22 different scenarios, with each image featuring 32 spectral bands and a spatial resolution of 10 meters per pixel. The dataset covers 22 locations in Australia, encompassing urban areas, mountainous regions, oceans, and other terrains. Compared to existing fire datasets, OHID-FF offers a richer volume of data and higher imaging quality, making it an ideal choice for training deep neural networks. Through benchmark experiments on this dataset, we found that existing methods face challenges in accurately classifying OHID-FF data, setting a new benchmark for hyperspectral imaging classification. Additionally, we provide detailed descriptions of the dataset preparation process, data sources, tile creation, and annotation procedures. Furthermore, we present experimental results using different deep learning models for fire detection and image classification, demonstrating the potential of this dataset in practical applications.