Early fire detection using non-linear multitemporal prediction of thermal imagery
Early fire detection using non-linear multitemporal prediction of thermal imagery
复制标题
DOI：
10.1016/j.rse.2007.02.010
复制
发表时间：
2007-09
影响因子：
13.5
通讯作者：
A. Koltunov;S. Ustin
中科院分区：
文献类型：
--
作者：
A. Koltunov;S. Ustin
本文提出了一种基于利用被检场景的多幅过去图像的非线性函数预测背景像素强度的亚像素热异常检测方法。目前，热异常探测的多时相方法正处于早期发展阶段。在星载监视的情况下，背景表面特性的空间和时间变化、天气影响、观察几何形状、传感器噪声、残余配准误差和其他因素使多时间检测复杂化。我们使用的问题，火灾探测和MODIS数据证明，先进的多时相检测方法可以潜在地优于操作使用的优化上下文算法在早晨和晚上的条件下。
This paper presents a sub-pixel thermal anomaly detection method based on predicting background pixel intensities using a non-linear function of a plurality of past images of the inspected scene. At present, the multitemporal approach to thermal anomaly detection is in its early development stage. In case of space-borne surveillance the multitemporal detection is complicated by both spatial and temporal variability of background surface properties, weather influences, viewing geometries, sensor noise, residual misregistration, and other factors. We use the problem of fire detection and the MODIS data to demonstrate that advanced multitemporal detection methods can potentially outperform the operationally used optimized contextual algorithms both under morning and evening conditions.