基于颜色和纹理特征的林火烟雾识别

Forest Fire Smoke Recognition Based on Color and Texture Features

为了实现森林火灾的智能化预警,提出了基于颜色和纹理特征的林火烟雾识别方法.首先使用颜色特征确定烟雾疑似区域,随后采用局部二值模式方差(Local Binary Pattern Variance,LBPV)提取疑似区域纹理的不规则度特征并产生LBP图像.然后利用小波变换分解LBP图像并提取模糊度、复杂度和相关度特征.最后利用支持向量机(Support Vector Machine,SVM)进行烟雾识别.结果证明,颜色结合纹理特征方法可以有效实现林火烟雾的识别,为林火烟雾识别研究提供了一种有效方案.

Aiming to implement the intelligent early warning of forest fire, a method based on color and texture features was proposed for forest fire smoke recognition. First, the color features were used to determine the smoke suspected area. Besides, the local binary pattern variance（LBPV） was utilized to extract the irregular feature of texture in the suspected area, and the LBP images was got. Wavelet transform was then used to extract the fuzzy, complex and correlative features from LBP images. At last, the fire smoke was identified by support vector machine（SVM）. The result demonstrated that the method based on color and texture features has good recognition of forest fire smoke, which provides an effective solution for the study of forest fire smoke recognition.