Forest fires are frequent natural disasters.It is necessary to explore advanced means to monitor,recognize and locate forest fires so as to establish a scientific system for the early detection,real-time positioning a...Forest fires are frequent natural disasters.It is necessary to explore advanced means to monitor,recognize and locate forest fires so as to establish a scientific system for the early detection,real-time positioning and quick fighting of forest fires.This paper mainly expounds methods and algorithms for improving accuracy and removing uncertainty in image-based forest fire recognition and spatial positioning.Firstly,we discuss a method of forest fire recognition in visible-light imagery.There are four aspects to improve accuracy and remove uncertainty in fire recognition:(1)eliminating factors of interference such as road and sky with high brightness,red leaves,other colored objects and objects that are lit up at night,(2)excluding imaging for specific periods and azimuth angles for which interference phenomena repeatedly occur,(3)improving the thresholding method for determining the flame border in image processing by adjusting the threshold to the season,weather and region,and (4)integrating the visible-light image method with infrared image technology.Secondly,we examine infrared-image-based methods and approaches of improving the accuracy of forest fire recognition by combining the spectrum threshold with an object feature value such as the normalized difference vegetation index and excluding factors of disturbance such as interference signals,extreme weather and high-temperature animals.Thirdly,a method of visible analysis to enhance the accuracy of forest fire positioning is examined and realized;the method includes decreasing the visual angle,selecting central points,selecting the largest spots,and judging the selection of fire spots according to the central distance.Case studies are examined and the results are found to be satisfactory.展开更多
在多目标群搜索算法(multi-objective group search optimization,MGSO)基本原理的基础上,结合Pareto最优解理论,提出了基于约束改进的多目标群搜索算法(IMGSO),并应用于多目标的结构优化设计.算法的改进主要有3个方面:第一,引入过渡可...在多目标群搜索算法(multi-objective group search optimization,MGSO)基本原理的基础上,结合Pareto最优解理论,提出了基于约束改进的多目标群搜索算法(IMGSO),并应用于多目标的结构优化设计.算法的改进主要有3个方面:第一,引入过渡可行域的概念来处理约束条件;第二,利用庄家法来构造非支配解集;最后,结合禁忌搜索算法和拥挤距离机制来选择发现者,以避免解集过早陷入局部最优,并提高收敛精度.采用IMGSO优化算法分别对平面和空间桁架结构进行了离散变量的截面优化设计,并与MGSO优化算法的计算结果进行了比较,结果表明改进的多目标群搜索优化算法IMGSO与MGSO算法相比具有更好的收敛精度.通过算例表明:IMGSO算法得到的解集中的解能大部分支配MGSO算法的解,在复杂高维结构中IMGSO算法的优越性更加明显,且收敛速度也有一定的提高,可有效应用于多目标的实际结构优化设计.展开更多
基金supported by the National High-Tech Research and Development Program of China("863"project)(Grant No.2006AA06Z418)
文摘Forest fires are frequent natural disasters.It is necessary to explore advanced means to monitor,recognize and locate forest fires so as to establish a scientific system for the early detection,real-time positioning and quick fighting of forest fires.This paper mainly expounds methods and algorithms for improving accuracy and removing uncertainty in image-based forest fire recognition and spatial positioning.Firstly,we discuss a method of forest fire recognition in visible-light imagery.There are four aspects to improve accuracy and remove uncertainty in fire recognition:(1)eliminating factors of interference such as road and sky with high brightness,red leaves,other colored objects and objects that are lit up at night,(2)excluding imaging for specific periods and azimuth angles for which interference phenomena repeatedly occur,(3)improving the thresholding method for determining the flame border in image processing by adjusting the threshold to the season,weather and region,and (4)integrating the visible-light image method with infrared image technology.Secondly,we examine infrared-image-based methods and approaches of improving the accuracy of forest fire recognition by combining the spectrum threshold with an object feature value such as the normalized difference vegetation index and excluding factors of disturbance such as interference signals,extreme weather and high-temperature animals.Thirdly,a method of visible analysis to enhance the accuracy of forest fire positioning is examined and realized;the method includes decreasing the visual angle,selecting central points,selecting the largest spots,and judging the selection of fire spots according to the central distance.Case studies are examined and the results are found to be satisfactory.
文摘在多目标群搜索算法(multi-objective group search optimization,MGSO)基本原理的基础上,结合Pareto最优解理论,提出了基于约束改进的多目标群搜索算法(IMGSO),并应用于多目标的结构优化设计.算法的改进主要有3个方面:第一,引入过渡可行域的概念来处理约束条件;第二,利用庄家法来构造非支配解集;最后,结合禁忌搜索算法和拥挤距离机制来选择发现者,以避免解集过早陷入局部最优,并提高收敛精度.采用IMGSO优化算法分别对平面和空间桁架结构进行了离散变量的截面优化设计,并与MGSO优化算法的计算结果进行了比较,结果表明改进的多目标群搜索优化算法IMGSO与MGSO算法相比具有更好的收敛精度.通过算例表明:IMGSO算法得到的解集中的解能大部分支配MGSO算法的解,在复杂高维结构中IMGSO算法的优越性更加明显,且收敛速度也有一定的提高,可有效应用于多目标的实际结构优化设计.
