Extracting the three-dimensional (3D) information including location and height of a pedestrian is important for vision-based intelligent traffic monitoring systems. This paper tackles the relationship between pixels...Extracting the three-dimensional (3D) information including location and height of a pedestrian is important for vision-based intelligent traffic monitoring systems. This paper tackles the relationship between pixels′ actual size and pixels′ spatial resolution through a new method named pixel-resolution mapping (P-RM). The proposed P-RM method derives the equations for pixels′ spatial resolutions (XY-direction) and object′s height (Z-direction) in the real world, while introducing new tilt angle and mounting height calibration methods that do not require special calibration patterns placed in the real world. Both controlled laboratory and actual world experiments were performed and reported. The tests on 3D mensuration using proposed P-RM method showed overall better than 98.7% accuracy in laboratory environments and better than 96% accuracy in real world pedestrian height estimations. The 3D reconstructed images for measured points were also determined with the proposed P-RM method which shows that the proposed method provides a general algorithm for 3D information extraction.展开更多
地上生物量(AGB)是评估作物生长发育和指导田间农业生产管理的重要指标。因此,高效精准地获取作物AGB信息,可以及时准确地估算产量,对于保障粮食供应和贸易提供有力依据。传统获取AGB的方法是采用破坏性取样法,这使得大面积、长期的测...地上生物量(AGB)是评估作物生长发育和指导田间农业生产管理的重要指标。因此,高效精准地获取作物AGB信息,可以及时准确地估算产量,对于保障粮食供应和贸易提供有力依据。传统获取AGB的方法是采用破坏性取样法,这使得大面积、长期的测量变为困难。然而,随着精准农业的快速发展,无人机遥感技术被认为是估算大面积作物AGB最有效的技术方式。通过无人机平台搭载多光谱传感器获取马铃薯块茎形成期、块茎增长期和淀粉积累期的多光谱影像,地面实测株高和AGB以及地面控制点(GCP)的空间位置信息。首先,基于SFM(structure from motion,SFM)技术利用无人机影像数据结合GCP的三维坐标生成试验田的DSM(digital surface model,DSM),通过DSM提取出马铃薯各生育期的株高(Hdsm);然后,选取原始4个单波段植被指数、9个多波段组合的植被指数、红边波段的高频信息(HFI)和提取的Hdsm分别与AGB作相关性分析;最后基于单波段植被指数(x_(1))、多波段组合的植被指数(x_(2))、植被指数结合Hdsm(x_(3))、植被指数结合HFI(x_(4))以及植被指数融合HFI和Hdsm(x_(5))为模型输入参数,采用偏最小二乘回归(PLSR)和岭回归(RR)估算各生育期的AGB。结果表明:(1)提取的Hdsm和实测株高拟合的R^(2)为0.87,NRMSE为14.34%;(2)各模型参数都与AGB达到极显著水平,相关性均从块茎形成期到淀粉积累期先升高后降低;(3)各生育期以5种变量使用同种方法估算马铃薯AGB的效果,均从块茎形成期到淀粉积累期先好后变差,其估算精度由高到低依次为x_(5)>x_(4)>x_(3)>x_(2)>x_(1);(4)各生育期使用PLSR以不同变量估算AGB的效果要优于RR方法,其中在块茎增长期基于x_(5)变量估算马铃薯AGB效果最佳,R^(2)为0.73,NRMSE为15.22%。因此,选取多光谱植被指数结合红边波段的高频信息和Hdsm并使用PLSR方法可以明显提高AGB的估算精度,这为大面积马铃薯作物AGB的�展开更多
文摘Extracting the three-dimensional (3D) information including location and height of a pedestrian is important for vision-based intelligent traffic monitoring systems. This paper tackles the relationship between pixels′ actual size and pixels′ spatial resolution through a new method named pixel-resolution mapping (P-RM). The proposed P-RM method derives the equations for pixels′ spatial resolutions (XY-direction) and object′s height (Z-direction) in the real world, while introducing new tilt angle and mounting height calibration methods that do not require special calibration patterns placed in the real world. Both controlled laboratory and actual world experiments were performed and reported. The tests on 3D mensuration using proposed P-RM method showed overall better than 98.7% accuracy in laboratory environments and better than 96% accuracy in real world pedestrian height estimations. The 3D reconstructed images for measured points were also determined with the proposed P-RM method which shows that the proposed method provides a general algorithm for 3D information extraction.
文摘地上生物量(AGB)是评估作物生长发育和指导田间农业生产管理的重要指标。因此,高效精准地获取作物AGB信息,可以及时准确地估算产量,对于保障粮食供应和贸易提供有力依据。传统获取AGB的方法是采用破坏性取样法,这使得大面积、长期的测量变为困难。然而,随着精准农业的快速发展,无人机遥感技术被认为是估算大面积作物AGB最有效的技术方式。通过无人机平台搭载多光谱传感器获取马铃薯块茎形成期、块茎增长期和淀粉积累期的多光谱影像,地面实测株高和AGB以及地面控制点(GCP)的空间位置信息。首先,基于SFM(structure from motion,SFM)技术利用无人机影像数据结合GCP的三维坐标生成试验田的DSM(digital surface model,DSM),通过DSM提取出马铃薯各生育期的株高(Hdsm);然后,选取原始4个单波段植被指数、9个多波段组合的植被指数、红边波段的高频信息(HFI)和提取的Hdsm分别与AGB作相关性分析;最后基于单波段植被指数(x_(1))、多波段组合的植被指数(x_(2))、植被指数结合Hdsm(x_(3))、植被指数结合HFI(x_(4))以及植被指数融合HFI和Hdsm(x_(5))为模型输入参数,采用偏最小二乘回归(PLSR)和岭回归(RR)估算各生育期的AGB。结果表明:(1)提取的Hdsm和实测株高拟合的R^(2)为0.87,NRMSE为14.34%;(2)各模型参数都与AGB达到极显著水平,相关性均从块茎形成期到淀粉积累期先升高后降低;(3)各生育期以5种变量使用同种方法估算马铃薯AGB的效果,均从块茎形成期到淀粉积累期先好后变差,其估算精度由高到低依次为x_(5)>x_(4)>x_(3)>x_(2)>x_(1);(4)各生育期使用PLSR以不同变量估算AGB的效果要优于RR方法,其中在块茎增长期基于x_(5)变量估算马铃薯AGB效果最佳,R^(2)为0.73,NRMSE为15.22%。因此,选取多光谱植被指数结合红边波段的高频信息和Hdsm并使用PLSR方法可以明显提高AGB的估算精度,这为大面积马铃薯作物AGB的�
