融合改进头脑风暴与Powell算法的马铃薯多模态图像配准  

Potato multimodal image registration by combining improved brain storm optimization algorithm and Powell algorithm

作　　者：李易达 王雨欣 李晨曦 赵冀 马恢 张漫[1] 李寒[2] LI Yida;WANG Yuxin;LI Chenxi;ZHAO Ji;MA Hui;ZHANG Man;LI Han(Key Laboratory of Modern Precision Agriculture System Integration Research,Ministry of Education,China Agricultural University,Beijing 100083,China;Key Laboratory of Agricultural Information Acquisition Technology,Ministry of Agriculture and Rural Affairs,China Agricultural University,Beijing 100083,China;Zhangjiakou Academy of Agricultural Sciences,Zhangjiakou 075000,China)

机构地区：[1]中国农业大学现代精细农业系统集成研究教育部重点实验室,北京100083 [2]中国农业大学农业农村部农业信息获取技术重点实验室,北京100083 [3]张家口市农业科学院,张家口075000

出　　处：《农业工程学报》2024年第19期146-158,共13页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(32171893)。

摘　　要：基于热成像仪获取作物冠层温度可以实现作物水分胁迫状态的非接触式、无损检测,并且具有高通量检测的潜力。然而热红外图像存在作物边缘分布不清晰、噪声强、缺乏形状、纹理信息等问题,无法实现作物冠层温度自动化提取,利用可见光与热红外图像间的信息互补性,通过图像自动配准技术可以弥补热红外图像缺点,为自动化检测提供基础。为解决可见光图像与热红外图像之间辐射、形状和纹理差异,导致不同模态图像配准难度较大问题,该研究提出了一种融合改进头脑风暴(brain storm optimization algorithm,BSO)与Powell算法的可见光与热红外图像配准方法。研究通过对原始BSO优化算法进行改进使得整体算法更好寻找到最优仿射变换矩阵进而完成图像配准任务,具体改进包含以下5个方面:使用混沌映射函数初始化BSO群体分布、修改新个体变异范围、手肘法动态调整BSO中K-means聚类数、在个体变异方式策略中加入混沌本地搜索方法、在算法执行过程中根据BSO算法前期后期不同特性动态调整概率参数。研究选用互信息值(mutual information,MI)、归一化互信息值(normalized mutual information,NMI)、均方根误差(root mean square error,RMSE)和平均结构相似性指数(mean structure similarity index measure,MSSIM)作为评价指标。该研究算法相对比Powell优化算法、遗传算法(genetic algorithm,GA)和BSO_Powell算法在温室数据中MI指标分别提升0.054 2、0.076 9、0.040 5,NMI指标分别提升0.015 9、0.023 1、0.052 7,RMSE指标分别降低15.02、13.03、27.08,MSSIM指标分别提升0.052 3、0.048 8、0.122 4;大田数据中MI指标分别提升0.064 2、0.066 7、0.035 5,NMI指标分别提升0.007 7、0.012 5、0.012 4,RMSE指标分别降低14.06、10.57、15.40,MSSIM指标分别提升0.047 1、0.038 1、0.042 9。结果表明,所提出算法具有很强的鲁棒性,能够准确完成复杂环境下马铃薯多模Crop canopy temperature can often be acquired using the thermal imager.Non-contact and non-destructive automated detection can be expected to achieve for crop water stress status.Automatic image alignment can be used to treat the fuzzy edge distribution,strong noise,as well as shape and texture information lacking in thermal infrared images,according to the information complementarity between visible light and thermal infrared images.The automated extraction can be realized on the crop canopy temperature.This study aims to solve the problems of differences in the radiation,shape,and texture between visible light images and thermal infrared images,leading to the low align images of different modalities.Multimodal image registration was also proposed to integrate the improved brain storm optimization(BSO)and Powell algorithm.Firstly,the original visible light image was downsampled and cropped,according to the normalized cross-correlation value.The area with the most similarity region was obtained in the thermal infrared image under the same resolution;Then,the target area was extracted from the cropped image.The target area image and the original thermal infrared image were decomposed by wavelet transform,where the multilayered low-frequency information was retained;Thirdly,the primitive affine transformation matrix was obtained by the image moments in the low-resolution layer;At the same time,the global search was used to optimize the affine transform matrix in the low-resolution layer using the improved BSO;Fourthly,the optimization was used as the initial point of the Powell algorithm.The optimization was performed in the high-resolution layer;Lastly,the optimization in the previous step was input into the Powell algorithm again.The original image layer was optimized again to obtain the final affine transformation matrix.The original BSO optimization was improved for the optimal affine transformation matrix in the image alignment task.The specific improvements included the following five aspects:The BSO populati

关 键 词：图像配准 头脑风暴算法 POWELL算法 互信息 马铃薯 

分 类 号：S24[农业科学—农业电气化与自动化]