由于缺乏大规模的雾天飞机目标遥感数据集,现有的目标检测方法难以在雾天条件下实现高精度的目标识别和定位任务。针对这一问题,提出了一种雾天条件下飞机目标检测方法,该方法结合了暗通道先验算法和Faster R⁃CNN(Faster Regions with C...由于缺乏大规模的雾天飞机目标遥感数据集,现有的目标检测方法难以在雾天条件下实现高精度的目标识别和定位任务。针对这一问题,提出了一种雾天条件下飞机目标检测方法,该方法结合了暗通道先验算法和Faster R⁃CNN(Faster Regions with Convolutional Neural Network Features)模型。首先,随机选取少量飞机目标原始图像,通过图像处理数据增强法扩展原始图像遥感数据集。其次,利用暗通道先验算法计算真实雾气图像的透射率值,并将其移植到原始图像中,生成雾气模拟的遥感数据集。最后,使用创建的数据集训练Faster R⁃CNN网络模型以完成飞机目标的识别和定位任务。实验结果表明,与原始数据集相比,该数据集在轻雾和浓雾状态下的检测性能都有明显提高,证明了所提数据集对于雾天环境下飞机目标检测的有效性和实用性。展开更多
Aerosol is one of the important geophysical parameters that determine the earth’s radiation budget, energy balance and hydrological cycle. The “Deep Blue” Moderate Resolution Imaging Spectro-radiometer (MODIS) Aero...Aerosol is one of the important geophysical parameters that determine the earth’s radiation budget, energy balance and hydrological cycle. The “Deep Blue” Moderate Resolution Imaging Spectro-radiometer (MODIS) Aerosol Optical Depth (AOD) retrieval algorithm was designed to complement existing “Dark Target” Ocean and Land algorithms to be able to retrieve AOD over bright land surface. Using level 2 AOD data from five Aerosol Robotic Network (AERONET) stations over the study location of North Africa (0°S - 40°N, 30°W - 60°E), comparative accuracy assessments are made for combined MODIS AOD aboard Terra and Aqua satellites and US Navy Aerosol Analysis and Prediction System (NAAPS) forecast AOD data. The aerosol transport and vertical mixing over the region are investigated at different altitudes up to 3000 m above ground level using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT). The MODIS validation result shows highest correlation in the Sub-Sahel (0.811) followed by Sahel (0.726) and then Sahara region (0.662). Furthermore, the combined retrieval algorithm of Terra and Aqua MODIS shows statistically significant discrepancies from AERONET AOD values in term of mean, t-test value, index of agreement and fractional error. The comparison of NAAPS predicted soil dust to AERONET AOD fared best in December to February (DJF) season for the Sahel region and June to August (JJA) season for the Sahara when the dust emission and transport are at the peak. However, median ratios of NAAPS to AERONET AOD indicated bias in some island sites in the Atlantic Ocean which may be due to the presence of sea salt over the site. The analysis carried out in this study reveals that both MODIS retrieval algorithm and NAAPS model could be improved by incorporating some local aerosol sources from the study area.展开更多
文摘由于缺乏大规模的雾天飞机目标遥感数据集,现有的目标检测方法难以在雾天条件下实现高精度的目标识别和定位任务。针对这一问题,提出了一种雾天条件下飞机目标检测方法,该方法结合了暗通道先验算法和Faster R⁃CNN(Faster Regions with Convolutional Neural Network Features)模型。首先,随机选取少量飞机目标原始图像,通过图像处理数据增强法扩展原始图像遥感数据集。其次,利用暗通道先验算法计算真实雾气图像的透射率值,并将其移植到原始图像中,生成雾气模拟的遥感数据集。最后,使用创建的数据集训练Faster R⁃CNN网络模型以完成飞机目标的识别和定位任务。实验结果表明,与原始数据集相比,该数据集在轻雾和浓雾状态下的检测性能都有明显提高,证明了所提数据集对于雾天环境下飞机目标检测的有效性和实用性。
文摘Aerosol is one of the important geophysical parameters that determine the earth’s radiation budget, energy balance and hydrological cycle. The “Deep Blue” Moderate Resolution Imaging Spectro-radiometer (MODIS) Aerosol Optical Depth (AOD) retrieval algorithm was designed to complement existing “Dark Target” Ocean and Land algorithms to be able to retrieve AOD over bright land surface. Using level 2 AOD data from five Aerosol Robotic Network (AERONET) stations over the study location of North Africa (0°S - 40°N, 30°W - 60°E), comparative accuracy assessments are made for combined MODIS AOD aboard Terra and Aqua satellites and US Navy Aerosol Analysis and Prediction System (NAAPS) forecast AOD data. The aerosol transport and vertical mixing over the region are investigated at different altitudes up to 3000 m above ground level using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT). The MODIS validation result shows highest correlation in the Sub-Sahel (0.811) followed by Sahel (0.726) and then Sahara region (0.662). Furthermore, the combined retrieval algorithm of Terra and Aqua MODIS shows statistically significant discrepancies from AERONET AOD values in term of mean, t-test value, index of agreement and fractional error. The comparison of NAAPS predicted soil dust to AERONET AOD fared best in December to February (DJF) season for the Sahel region and June to August (JJA) season for the Sahara when the dust emission and transport are at the peak. However, median ratios of NAAPS to AERONET AOD indicated bias in some island sites in the Atlantic Ocean which may be due to the presence of sea salt over the site. The analysis carried out in this study reveals that both MODIS retrieval algorithm and NAAPS model could be improved by incorporating some local aerosol sources from the study area.
