Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information

Abstract

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.

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“…The implemented CNN captures the spatial characteristics of geographic objects by modeling the nonlinear relationship between coarse remote sensing images and fine-scale land cover maps. On the other hand, super-resolution mapping is also studied in [8] for burned area mapping based on space-temperature information. The proposed method uses the random walker algorithm to characterize the space element; the temperature element is derived by calculating the normalized burn ratio; and both elements are merged in an objective function that is handled by using the particle swarm optimization algorithm to derive the burned area mapping results.…”