The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 215–222, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-215-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 215–222, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-215-2021

  28 Jun 2021

28 Jun 2021

ANALYSIS AND DETECTION OF WILDFIRES BY USING PRISMA HYPERSPECTRAL IMAGERY

D. Spiller1,3, L. Ansalone1, S. Amici2, A. Piscini2, and P. P. Mathieu3 D. Spiller et al.
  • 1Italian Space Agency, Rome, Italy
  • 2Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione ONT, 00143 Rome, Italy
  • 3Φ-Lab, EOP, European Space Agency, ESRIN, Frascati, Rome, Italy

Keywords: PRISMA, hyperspectral imagery, fire detection, multiclass classification, convolutional neural network

Abstract. This paper deals with the analysis and detection of wildfires by using PRISMA imagery. Precursore IperSpettrale della Mis­sione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 0.4–2.5 µm and an average spectral resolution less than 10 nm. In this work, we used the PRISMA hypercube acquired during the Australian bushfires of December 2019 in New South Wales. The analysis of the image is presented considering the unique amount of information contained in the continuous spectral signature of the hypercube. The Carbon dioxide Continuum-Interpolated Band Ratio (CO2 CIBR), Hyperspectral Fire Detection Index (HFDI), and Normalized Burn Index (NBR) will be used to analyze the informative content of the image, along with the analysis of some specific visible, near-infrared and shortwave-infrared bands. A multiclass classification is presented by using a I-dimensional convolutional neural network (CNN), and the results will be com­pared with the ones given by a support vector machine classifier reported in literature. Finally, some preliminary results related to wildfire temperature estimation are presented.