Harris角点自适应检测的水稻低空遥感图像配准与拼接算法  

Image registration and stitching algorithm of rice low-altitude remote sensing based on Harris corner self-adaptive detection

作　　者：周志艳[1,2] 闫梦璐[1] 陈盛德 兰玉彬[1] 罗锡文[1] 

机构地区：[1]华南农业大学工程学院/广东省农业航空应用工程技术研究中心,广州510642 [2]南方粮油作物协同创新中心,长沙410128

出　　处：《农业工程学报》2015年第14期186-193,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金："十二五"国家"863"计划项目(2013AA102303;2012AA101901-3);国家自然科学基金项目(31371539)

摘　　要：图像配准和拼接的自动化是微小型无人机能否被广泛应用于水稻长势低空遥感监测的关键技术之一。为了改进Harris角点检测算法中阈值需要人为设定的局限,文章提出了基于Harris角点自适应检测的水稻低空遥感图像配准与拼接算法。该算法在Harris角点检测算法的基础上进行改进,采用基于图像像素灰度值标准差标准化的方法进行角点的自适应确定,并对角点进行特征描述,利用角点特征描述算子之间的欧式距离进行配准。为了验证算法的有效性并进行相关参数的优化,采用多旋翼无人直升机获取了水稻长势的低空遥感图像,并设计了重复率(衡量角点检测的稳定性)、辨识率(衡量角点描述算子的辨识度)、配准率(衡量图像的拼接精度)以及运行时间(衡量算法的运算速度)4个评价指标对配准与拼接的结果进行评判。随机选取获得的低空遥感图像分成3组进行测试,试验结果表明,平均配准率达到了98.95%,且各组图像之间的重复率与配准率差异不显著(显著性水平为0.05),说明改进后的算法稳定。设计了角点自适应检测算法阈值参数的优选试验,阈值参数为标准化处理后的图像像素灰度值标准差,方差分析结果表明,图像像素灰度标准差为1和2时配准率的差异不显著(显著性水平为0.05),但当图像像素灰度标准差为1时,图像配准与拼接平均运行时间是其为2时的2.5倍,因此,可设定图像像素灰度标准差为2作为本算法的较优参数。Automation of images registration and stitching is one of the most important key technologies to the wide use of the low-altitude remote sensing by Micro-UAVs （unmanned aerial vehicles） in rice growing. In order to overcome the limitations, i.e. the thresholds need to be artificially determined for the traditional Harris corner detection algorithm, this paper proposed a self-adaptive algorithm for Harris corner detection, which was used in image registration and stitching of the rice low-altitude remote sensing. The algorithm was improved based on the traditional Harris corner detection algorithm by using a self-adaptive threshold determination method, which calculated from the standard deviation of image pixel gray-scale value. And then the characteristics of image were described by corners, and the images were registered by using the Euclidean distance among descriptors. In order to verify the effectiveness of the algorithm and optimize the relevant parameters, a verification test was conducted based on low-altitude remote sensing images, which were gained by a multispectral camera mounted on a multi-rotor unmanned helicopter during rice tillering stage. Four indices, the repetition rate （a measure of the stability of corner detection）, the recognition rate （a measure of corner recognizable description operator）, the registration rate （a measure of the accuracy of image registration and stitching） and running time of algorithm （a measure of computing speed of the algorithm）, were proposed to evaluate the results of registration and stitching. Sixty images were randomly divided into 3 groups for verification test. Test results showed that the average registration rate reached 98.95%, and also the average repetition rate reached 96%, which indicated that the proposed algorithm had high accuracy. The repetition rate and the difference in image registration rates among the groups were not significant （at 0.05 significance level）, which indicated that the proposed algorithm was stable and rel

关 键 词：图像配准 遥感 自适应算法 图像拼接 角点检测 自适应检测 低空遥感图像 

分 类 号：S127[农业科学—农业基础科学] S252.9