Multispectral image super-resolution burned-area mapping based on space-temperature information
Document Type: Journal Article
Author(s): Peng Wang; Rei ZhG; Gong Zhang; Benzhou Jin; Henry Leung
Publication Year: 2019

Cataloging Information

  • burned area
  • Denali National Park and Preserve
  • multispectral imagery
  • NBR - Normalized Burn Ratio
  • random-walker algorithm
  • space-temperature information
Record Maintained By:
Record Last Modified: November 30, 2019
FRAMES Record Number: 59097


Multispectral imaging (MI) provides important information for burned-area mapping. Due to the severe conditions of burned areas and the limitations of sensors, the resolution of collected multispectral images is sometimes very rough, hindering the accurate determination of burned areas. Super-resolution mapping (SRM) has been proposed for mapping burned areas in rough images to solve this problem, allowing super-resolution burned-area mapping (SRBAM). However, the existing SRBAM methods do not use sufficiently accurate space information and detailed temperature information. To improve the mapping accuracy of burned areas, an improved SRBAM method utilizing space-temperature information (STI) is proposed here. STI contains two elements, a space element and a temperature element. We utilized the random-walker algorithm (RWA) to characterize the space element, which encompassed accurate object space information, while the temperature element with rich temperature information was derived by calculating the normalized burn ratio (NBR). The two elements were then merged to produce an objective function with space-temperature information. The particle swarm optimization algorithm (PSOA) was employed to handle the objective function and derive the burned-area mapping results. The dataset of the Landsat-8 Operational Land Imager (OLI) from Denali National Park, Alaska, was used for testing and showed that the STI method is superior to the traditional SRBAM method.

Online Link(s):
Wang, Peng; ZhG, Rei; Zhang, Gong; Jin, Benzhou; Leung, Henry. 2019. Multispectral image super-resolution burned-area mapping based on space-temperature information. Remote Sensing 11(22):2695.