基于样条的林火图像多阈值分割算法

摘要

基于光滑样条原理,提出一种自适应的多阈值分割算法HistSplineReg,即采用光滑样条回归图像一维直方图,再从回归函数寻找极值从而实现图像的多阈值自动分割.较之现有的阈值分割方法,HistSplineReg具有以下优势:1)设计方法符合人类直觉;2)基于光滑样条设计算法,有理论依据;3)阈值个数及阈值位置可自动选择;4)回归函数可分析求解,计算规模主要集中在矩阵的Cholesky分解,矩阵大小由图像像素水平级决定,而不是图像尺寸;5)只有一个待定参数,该参数用于平衡回归经验误差和回归函数的光滑性.对林火识别问题,实验提供一个经验参数供参考.最后,在红绿蓝颜色(RGB)模式的林火数字图像上进行实验,从灰度图像、多种颜色通道、各通道分割结果合成的彩色图像等方面进行验证,与同样采样回归思想的支持向量回归(SVR)及多项式回归(PolyFit)相比,HistSplineReg方法直观分割效果更好,且三种方法都反映出红色通道信息对林火图像分割效果的影响更为显著.%Based on smooth spline principle,a self-adaptive multi-threshold segmentation algorithm HistSplineReg (Spline Regression for Histogram) was proposed.HistSplineReg is a two-step method.Firstly,a smoothing spline function was regressed to fit the one-dimensional image histogram,and then the extreme value was found by the regression function to achieve multi-threshold autoraatic segmentation of the image.Compared to the existing multi-threshold methods,the advantages of HistSplineReg lie in 5 aspects:1) it is quite consistent with the human intuition;2) it is constructed on the smoothing spline,which is a solid mathematic basis;3) both the number and the size of multiple thresholds can be automatically determined;4) HistSplineReg can be analytically solved,and its computing burden is mainly concentrated on Cholesky decomposition of the matrix,while the size of matrix depends on the pixel level of the image,rather than the scale of the image;5) it has only one trade-off parameter to balance the empirical error and regressor's smoothness.Furthermore,for the forest fire recognition task,an experimental reference value was provided.Finally,experiments were conducted on some digital forest fire images in the RGB (Red,Green,Blue) mode.The experimental results show that the histSplineReg method is more effective than Support Vector Regression (SVR) and Polynomial Fitting (PolyFit),which is based on the grayscale image,the color charmel,the color image synthesized by each channel segmentation.And the three methods all reflect the red channel information is most significant to the forest fire image segmentation effect.