基于高空间分辨率图像的森林监测烟尘分类

Julia Åhlén
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引用次数: 1

摘要

森林火灾对人类栖息地和森林生态系统造成重大破坏。及早发现可以防止火势迅速蔓延的严重后果。尽管各种光学遥感系统采用了许多烟雾检测算法,但对含雾图像的误检仍然存在较大的问题。雾表现出与烟相似的视觉特征。此外,当监测茂密的森林时,由于黎明时树木被雾覆盖,许多烟雾检测算法将无法进行鲁棒识别。已经有或多或少成功的尝试在光学图像中分离烟雾和雾,然而,这些算法与特定的应用领域紧密相关,或者使用半自动方法。本文提出了一种基于颜色空间模型计算和基于规则的形状分析的烟尘分离算法。此外,对候选烟区的内部特性进行了向高能量波长线性衰减的研究。然后研究这些区域的内部形状属性,如凸壳和偏心。对各种高分辨率航空图像进行的几次测试表明,该系统在区分烟雾和雾方面是有效的,因此被认为在森林地区的早期火灾探测方面是强大的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SMOKE AND FOG CLASSIFICATION IN FOREST MONITORING USING HIGH SPATIAL RESOLUTION IMAGES
Forest fires cause major damage to human habitats and forest ecosystems. Early detection may prevent serious consequences of fast fire spread. Although there are many smoke detection algorithms employed by various optical remote sensing systems, there is still a major misdetection of images containing fog. Fog exhibits similar visual characteristics to smoke. Furthermore, when monitoring dense forests many smoke detection algorithms would fail in robust recognition due to fog covering the trees at dawn. There have been more or less successful attempts to separate smoke from a fog in optical imagery however, these algorithms are strongly connected to a specific application area or use a semiautomatic approach. This work aims to propose a novel smoke and fog separation algorithm based on color space model calculation followed by rule-based shape analysis. In addition, the internal properties of the smoke candidate areas are examined for linear attenuation towards higher energy wavelength. Those areas are then investigated for internal shape properties such as convex hull and eccentricity. Several tests conducted on various high-resolution aerial images suggest that the system is effective in differentiating smoke and fog and thus considered to be robust in early fire detection in forest areas.
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