An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data

Lin, Zhengyang; Fang, Chen; Niu, Zheng; Li, Bin; Yu, Bo; Jia, Huicong; Zhang, Meimei

doi:10.1016/j.rse.2018.04.027

Cited by 44 publications

(26 citation statements)

References 38 publications

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“…Most recently, new algorithms have been developed including the temporal dimension in their classification process (Schroeder et al 2016), in order to discriminate between permanent sources of heat (bright surfaces, gas flares, etc.) and fire events (Lin et al 2018).…”

Section: Early Detection Of Fire Occurrence From Spacementioning

confidence: 99%

Fire Danger Observed from Space

Pettinari

Chuvieco

2020

Surv Geophys

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Biomass burning is one of the critical components of the Earth system, significantly affecting atmospheric emissions and carbon budgets. Fires occurring in the interface between wildland and urban areas also have important socioeconomic effects, affecting people's lives and resources. Even though fires are natural in many ecosystems, climate and societal changes have recently caused particularly severe fire seasons (Australia, California, Amazonia, Portugal…). Mitigating the negative impacts of fire requires further efforts to assess fire danger conditions. Satellite Earth observation provides considerable capabilities to evaluate the different variables involved in fire danger. Data obtained from remote sensors offer information on possible sources of fire ignition, on fuel status and abundance, and on the topography and the meteorological conditions that will affect fire spread. Satellite observations also provide near-real-time information on fire occurrence for early response teams. This article describes the different variables affecting fire danger and illustrates how satellite data can offer useful information to estimate these variables, focusing on global and continental fire danger systems.

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Section: Early Detection Of Fire Occurrence From Spacementioning

confidence: 99%

Fire Danger Observed from Space

Pettinari

Chuvieco

2020

Surv Geophys

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“…Fire detection will be affected by fire-like objects (e.g., automobile taillights, streetlamps at night) and the rising and setting Sun. Different types of fires, such as a solid fire, a gas fire, and a charged fire, vary greatly in their imagery, making it more difficult to effectively extract key information in fire images [11]. Existing fire detection research based on videos detects motion regions and only excludes the disturbance of static fire-like objects, which also tends to extract the static features of fires.…”

Section: Related Workmentioning

confidence: 99%

Fire Detection and Recognition Optimization Based on Virtual Reality Video Image

Xin-chu

2020

IEEE Access

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Fire detection technology based on video images can avoid many flaws in conventional methods and detect fires. To achieve this, the support vector machine (SVM) method in machine learning theory has unique advantages, while rough set (RS) theory and SVM complement each other in application. Thus, a new classifier could be created by organically combining these methods to identify fires and provide fire warnings, yielding excellent noise suppression and promotion. Therefore, in this study, an RS is used as the front-end system for the SVM method, yielding improved performance than only SVM. Recognition time is reduced, and recognition efficiency is improved. Experiments show that the RS-SVM classifier model based on parameter optimization proposed in this paper mitigates deficiencies in overfitting and determining local extremum with excellent reliability and stability, and enhances the forecast accuracy of fires. The method also reduces false fire-detection alarms and uses fire feature selection in virtual reality (VR) video images and fire detection and recognition.

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“…According to the application statistics during the year of 2018, the algorithm provides an omission error of less than 10%. In terms of sensitivity to radiative temperature variations, better results will be obtained when the temperature contrast 6 Mathematical Problems in Engineering between wildfires and the background environment increased. It is possible to draw a conclusion that the proposed algorithm can also be effective when applied to other similar sensors, such as AVHRR and MODIS, to take full advantage of the available data and reduce errors to a minimum.…”

Section: Regional Performancementioning

confidence: 99%

“…For more than three decades, the spatial, spectral, and temporal characteristics of remote sensing detectors have been constantly improving, and new methods for fire detection systems have been developed [4][5][6]. e Advanced Very High Resolution Radiometer (AVHRR) [7,8] was the first applied to provided mid-infrared (MIR) and thermal infrared (TIR) data to assess temperature variations of earth.…”

Section: Introductionmentioning

confidence: 99%

A Self-Adaptive Wildfire Detection Algorithm with Two-Dimensional Otsu Optimization

Zhang

Luo

et al. 2020

Mathematical Problems in Engineering

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The gradual increase in wildfires has caused frequent trips and outages along electrical transmission lines, which is a serious threat to the operational stability of power grids. A self-adaptive wildfire detection algorithm has been developed and tested in this paper. Most of existing wildfire detection methods employed fixed thresholds to identify potential wildfire pixels while the background pixels were ignored. By calculating two-dimensional histogram of the brightness temperatures of mid-infrared channel, the threshold selection is self-adaptive and potential pixels containing scenes of fire can be distinguished automatically. Based on the two-dimensional Otsu method and contextual test algorithm, an improved wildfire detection algorithm that uses multitemporal Visible and Infrared Radiometer (VIRR) data is described. The wildfire detection results within three kilometers of electrical transmission lines demonstrate the effectiveness of the proposed method, which has accurate low-temperature wildfire detection ability.

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An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data

Cited by 44 publications

References 38 publications

Fire Danger Observed from Space

Fire Danger Observed from Space

Fire Detection and Recognition Optimization Based on Virtual Reality Video Image

A Self-Adaptive Wildfire Detection Algorithm with Two-Dimensional Otsu Optimization

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