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
可见光植被指数是一种基于RGB影像的植被提取方法,目前已被广泛用于无人机影像植被提取,现有的方法在植被提取效率及增大植被与其他地物区分度方面仍有可改进的空间。基于绿色健康植被光谱特性及8种不同地物在无人机RGB影像中的光谱特征...可见光植被指数是一种基于RGB影像的植被提取方法,目前已被广泛用于无人机影像植被提取,现有的方法在植被提取效率及增大植被与其他地物区分度方面仍有可改进的空间。基于绿色健康植被光谱特性及8种不同地物在无人机RGB影像中的光谱特征,提出一种基于绿、蓝波段的可见光植被指数——超绿蓝比值指数(enhanced green blue ratio index,EGBRI),利用该指数与其他8种常见可见光植被指数提取效果进行对比研究,并采用基于目视解译的地物判别结果结合混淆矩阵进行精度量化评价。结果表明:由EGBRI计算的植被指数能够有效提取试验区绿色植被,对其他地物具有抑制作用;相比其他常见可见光植被指数,EGBRI增强了植被与其他地类的区分度,其分类精度更高,EGBRI总体精度为95.06%,Kappa系数为0.8895,处于较高水平,能够对试验区的植被覆盖区域进行快速、准确的提取。研究结果表明,提出的超绿蓝比值指数(EGBRI)能够有效、快速、高精度、大范围地提取无人机影像RGB波段影像中绿色植被信息,且具有较好的适用性和提取精度。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.
文摘可见光植被指数是一种基于RGB影像的植被提取方法,目前已被广泛用于无人机影像植被提取,现有的方法在植被提取效率及增大植被与其他地物区分度方面仍有可改进的空间。基于绿色健康植被光谱特性及8种不同地物在无人机RGB影像中的光谱特征,提出一种基于绿、蓝波段的可见光植被指数——超绿蓝比值指数(enhanced green blue ratio index,EGBRI),利用该指数与其他8种常见可见光植被指数提取效果进行对比研究,并采用基于目视解译的地物判别结果结合混淆矩阵进行精度量化评价。结果表明:由EGBRI计算的植被指数能够有效提取试验区绿色植被,对其他地物具有抑制作用;相比其他常见可见光植被指数,EGBRI增强了植被与其他地类的区分度,其分类精度更高,EGBRI总体精度为95.06%,Kappa系数为0.8895,处于较高水平,能够对试验区的植被覆盖区域进行快速、准确的提取。研究结果表明,提出的超绿蓝比值指数(EGBRI)能够有效、快速、高精度、大范围地提取无人机影像RGB波段影像中绿色植被信息,且具有较好的适用性和提取精度。
