Fire burned area identification method adopting spectral index and neural network combined

Abstract

The invention discloses a fire burned area identification method adopting the spectral index and a neural network combined. Through analysis on spectral characteristics of different ground object classes, the spectral index with the optimal separability for all the ground object classes is adopted as the input characteristic of the neural network, and the distinction degree of the pixel of a burned area and other classes of pixels can be increased; and meanwhile, the spectral index serves as a training sample to construct a classifier, thus burned area identification can be conducted on a satellite image, and applicability and accuracy are improved.

Description

technical field [0001] The invention relates to the field of fire disaster assessment, in particular to a method for identifying fire traces combined with spectral index and neural network. Background technique [0002] In recent years, natural fires are still one of the major disasters in the world today, posing a great threat to the safety of human life and property, biodiversity and ecological environment. Forest and grassland fires are sudden and destructive. Once they break out, they are difficult to control and can easily cause heavy loss of life and property. The burning of biomass releases a large amount of greenhouse gases (such as CO 2 , CO, CH 4 , NO X etc.) and aerosols, while changing the chemical composition of the atmosphere, they also directly affect the global climate system. At the same time, natural fires will also destroy biological habitats and biodiversity, and affect the successional growth process of vegetation. Fire slashes are the areas where v...

AI Technical Summary

Problems solved by technology

However, the above-mentioned algorithm based on a single remote sensing image is still not thorough enough to study the separation of different spectral bands and spectral indices for various ground object categories, and pixel classification is prone to misjudgment and missed judgment, which leads to a decrease in the accuracy of the algorithm

[0007] Generally speaking, the existing fire trace identification technology is still weak in the research on the spectral characteristics of different ground objects, and the accuracy of the algorithm still needs to be improved

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