Unmanned aerial vehicle (UAV) target tracking tasks can currently be successfully completed in daytime situations with enough lighting, but they are unable to do so in nighttime scenes with inadequate lighting, poor c...Unmanned aerial vehicle (UAV) target tracking tasks can currently be successfully completed in daytime situations with enough lighting, but they are unable to do so in nighttime scenes with inadequate lighting, poor contrast, and low signal-to-noise ratio. This letter presents an enhanced low-light enhancer for UAV nighttime tracking based on Zero-DCE++ due to its ad-vantages of low processing cost and quick inference. We developed a light-weight UCBAM capable of integrating channel information and spatial features and offered a fully considered curve projection model in light of the low signal-to-noise ratio of night scenes. This method significantly improved the tracking performance of the UAV tracker in night situations when tested on the public UAVDark135 and compared to other cutting-edge low-light enhancers. By applying our work to different trackers, this search shows how broadly applicable it is.展开更多
株高和叶面积指数(Leaf Area Index,LAI)反映着作物的生长发育状况。为了探究基于无人机可见光遥感提取冬小麦株高的可靠性,以及利用株高和可见光植被指数估算LAI的精度,本文获取了拔节期、抽穗期、灌浆期的无人机影像,提取了冬小麦株...株高和叶面积指数(Leaf Area Index,LAI)反映着作物的生长发育状况。为了探究基于无人机可见光遥感提取冬小麦株高的可靠性,以及利用株高和可见光植被指数估算LAI的精度,本文获取了拔节期、抽穗期、灌浆期的无人机影像,提取了冬小麦株高与可见光植被指数,使用逐步回归、偏最小二乘、随机森林、人工神经网络四种方法建立LAI估测模型,并对株高提取及LAI估测情况进行精度评价。结果显示:(1)株高提取值Hc与实测值Hd高度拟合(R^(2)=0.894,RMSE=6.695,NRMSE=9.63%),株高提取效果好;(2)与仅用可见光植被指数相比,基于株高与可见光植被指数构建的LAI估测模型精度更高,且随机森林为最优建模方法,当其决策树个数为50时模型估测效果最好(R^(2)=0.809,RMSE=0.497,NRMSE=13.85%,RPD=2.336)。利用无人机可见光遥感方法,高效、准确、无损地实现冬小麦株高及LAI提取估测可行性较高,该研究结果可为农情遥感监测提供参考。展开更多
文摘Unmanned aerial vehicle (UAV) target tracking tasks can currently be successfully completed in daytime situations with enough lighting, but they are unable to do so in nighttime scenes with inadequate lighting, poor contrast, and low signal-to-noise ratio. This letter presents an enhanced low-light enhancer for UAV nighttime tracking based on Zero-DCE++ due to its ad-vantages of low processing cost and quick inference. We developed a light-weight UCBAM capable of integrating channel information and spatial features and offered a fully considered curve projection model in light of the low signal-to-noise ratio of night scenes. This method significantly improved the tracking performance of the UAV tracker in night situations when tested on the public UAVDark135 and compared to other cutting-edge low-light enhancers. By applying our work to different trackers, this search shows how broadly applicable it is.
文摘株高和叶面积指数(Leaf Area Index,LAI)反映着作物的生长发育状况。为了探究基于无人机可见光遥感提取冬小麦株高的可靠性,以及利用株高和可见光植被指数估算LAI的精度,本文获取了拔节期、抽穗期、灌浆期的无人机影像,提取了冬小麦株高与可见光植被指数,使用逐步回归、偏最小二乘、随机森林、人工神经网络四种方法建立LAI估测模型,并对株高提取及LAI估测情况进行精度评价。结果显示:(1)株高提取值Hc与实测值Hd高度拟合(R^(2)=0.894,RMSE=6.695,NRMSE=9.63%),株高提取效果好;(2)与仅用可见光植被指数相比,基于株高与可见光植被指数构建的LAI估测模型精度更高,且随机森林为最优建模方法,当其决策树个数为50时模型估测效果最好(R^(2)=0.809,RMSE=0.497,NRMSE=13.85%,RPD=2.336)。利用无人机可见光遥感方法,高效、准确、无损地实现冬小麦株高及LAI提取估测可行性较高,该研究结果可为农情遥感监测提供参考。
