The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 447–452, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-447-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 447–452, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-447-2020

  21 Aug 2020

21 Aug 2020

A SIMPLE ARTIFICIAL NEURAL NETWORK FOR FIRE DETECTION USING LANDSAT-8 DATA

Z. Liu1, K. Wu1, R. Jiang1, and H. Zhang1,2,3 Z. Liu et al.
  • 1Department of Aerospace Information Engineering, School of Astronautics, Beihang University, 102206 Beijing, China
  • 2Beijing Key Laboratory of Digital Media, 102206 Beijing, China
  • 3Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, 102206 Beijing, China

Keywords: Fire Detection, Artificial Neural Network, Fixed Threshold, Landsat-8, Remote Sensing Image Classification

Abstract. Fixed threshold models have been widely used in active fire detection products. However, its accuracy is limited due to the complexity of setting up thresholds. Artificial neural network (ANN) is capable of learning from data and can decide weights automatically. Given enough data, an ANN model is able to optimize itself and quickly find an optimal solution. In this work, a simple ANN model is implemented to classify fire pixels from Landsat-8 data. Experimental results show that our ANN model effectively achieves fire detection and performs better than fixed threshold model in certain circumstances.