基于逻辑回归算法的复杂背景棉田冠层图像自适应阈值分割  

Adaptive threshold segmentation for cotton canopy image in complex background based on logistic regression algorithm

作　　者：刘立波[1] 程晓龙[1] 戴建国[2] 赖军臣 

机构地区：[1]宁夏大学信息工程学院,银川750021 [2]石河子大学信息科学与技术学院,石河子832000 [3]新疆五家渠市农业局,五家渠831300

出　　处：《农业工程学报》2017年第12期201-208,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(31460317)

摘　　要：棉田冠层覆盖度是监测棉田棉花长势的重要指标,针对棉田复杂环境中冠层图像难以准确分割的问题,该文提出了一种基于逻辑回归算法的复杂背景棉田冠层图像自适应阈值分割方法。首先将棉田冠层图像像素分成叶片冠层和地表背景2类,在HSV颜色空间中分别提取两类像素的H通道值,在RGB颜色空间中分别提取绿色占比值(G/(G+R+B))作为颜色特征;再利用逻辑回归算法确定出各颜色特征的分割阈值,通过H通道分割阈值实现图像的初次分割;再对初次分割结果中的低亮像素使用逻辑回归算法计算出的超绿特征阈值进行低亮像素分割,同时采用绿色占比分割阈值对图像高亮像素及低亮像素分割结果整体实现二次分割,最后采用形态学滤波方法对分割结果进行优化。为评价该分割方法,利用从新疆棉花产区采集到的320幅棉田冠层图像进行试验。结果表明,该方法可在棉田复杂自然背景下,有效分割出棉田冠层区域,平均相对目标面积误差率仅为5.46%,总体平均匹配率达到93.07%;优于超绿特征OTSU分割方法(平均相对目标面积误差率11.78%,总体平均匹配率76.43%)、四分量分割方法(平均相对目标面积误差率24.11%,总体平均匹配率71.67%)、显著性分割方法(平均相对目标面积误差率36.92%,总体平均匹配率66.92%)。该方法的平均处理时间为4.63 s,相对于超绿特征OTSU法(3.84 s)和四分量分割法(2.56 s),耗时多一些,但与显著性分割法(6.25 s)对比,花费时间要少。研究结果可为棉田自然复杂环境下机器视觉技术监测棉花覆盖度提供一种有效途径。Cotton canopy coverage is an important index for monitoring cotton growing in field. It is easy but not accurate to calculate, because it is difficult to accurately segment the cotton canopy in the complex environment image of cotton field. This paper presents an adaptive threshold segmentation approach of cotton canopy image based on logistic algorithm in order to improve the segmentation precision and robusmess for cotton canopy image. Firstly, the cotton canopy image is transformed into HSV （hue, saturation, value） color space. This color space is designed by human color description. In this color space, the color feature of the pixel can be expressed by 3 independent components i.e. H, S and V. In this paper, the logistic regression algorithm is used to compute threshold used in image segmentation. The logistic regression algorithm is often used in 2 kinds of classification problem, so our method need an artificially defined variable. This variable and a single color feature variable can form a dataset as the input of logical regression algorithm to calculate the segmentation threshold. In our paper, the proposed artificially defined variable is set to a specific value that is 1, the effect of which is to reduce the impact for computed segmentation threshold. The cotton canopy image＇s pixel is divided into 2 classes： target and background. The H channel feature of 2 classes can be extracted in HSV color space, and the green ratio （G/（R＋G＋B）） of 2 classes can be extracted in RGB （red, green, blue） color space. Those features＇ thresholds are computed by logistic regression algorithm. H channel thresholds are used to achieve the first segmentation. Secondly, the first segmentation result is divided to highlight pixels and low pixels. The highlight pixels mainly include light canopy and light soil, and the low pixels mainly include shadow canopy and shadow soil. However, it is difficult to segment cotton canopy in the low pixels. In order to solve this problem, extra-green （Exg） colo

关 键 词：算法 棉花 图像分割 逻辑回归 自适应阈值 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]