A P-band polarimetric synthetic aperture radar(PolSAR)sensor has deep penetration ability into and through the vegetation canopies in forested environments.Thus,the sensor is of great potential to accurately assess fo...A P-band polarimetric synthetic aperture radar(PolSAR)sensor has deep penetration ability into and through the vegetation canopies in forested environments.Thus,the sensor is of great potential to accurately assess forest parameters such as coverage,stand density,and tree height.Unfortunately,the radar backscatter from complex terrain can adversely impact the backscatter from trees or forests,and forest parameters assessed can be erroneous.Thus,reducing the topographic impact is an urgent must.In this study,a topographic compensation algorithm has been studied.To assess the algorithm’s validity and effectiveness,we applied it to P-band PolSAR datasets in four forested areas in the US.Trees in the forest stands have diverse species,and the topographic conditions of the terrain differ.Significant topographic impact on the P-band PolSAR data exists before the topographic compensation algorithm.After the algorithm,the impact decreases noticeably qualitatively and quantitatively.The algorithm is valid and effective in reducing the topographic influence on the PolSAR data and,consequently,provides a better chance of retrieving accurate forest parameters.展开更多
针对传统PolSAR影像建筑区域提取方法对影像特征利用不充分、自动化程度不高的问题,研究一种基于全卷积网络(fully convolutional networks,FCN)和条件随机场(conditional random field,CRF)相结合的建筑区域提取方法。该方法充分利用FC...针对传统PolSAR影像建筑区域提取方法对影像特征利用不充分、自动化程度不高的问题,研究一种基于全卷积网络(fully convolutional networks,FCN)和条件随机场(conditional random field,CRF)相结合的建筑区域提取方法。该方法充分利用FCN网络对影像进行逐像素分类并能自动提取影像高层特征的优势,首先通过制作样本集对FCN网络进行训练;然后利用训练好的模型进行初步的建筑区域提取;最后利用可以联系上下文信息的条件随机场CRF对结果进行优化处理。实验结果表明,该方法可以充分利用影像的语义信息,有效地减少孤立点,提高对细节、轮廓的提取精度,获得较高精度的建筑区域提取结果。展开更多
基金supported by the National Natural Science Foundation of China under Grants No.41771401 and No.42350710201.
文摘A P-band polarimetric synthetic aperture radar(PolSAR)sensor has deep penetration ability into and through the vegetation canopies in forested environments.Thus,the sensor is of great potential to accurately assess forest parameters such as coverage,stand density,and tree height.Unfortunately,the radar backscatter from complex terrain can adversely impact the backscatter from trees or forests,and forest parameters assessed can be erroneous.Thus,reducing the topographic impact is an urgent must.In this study,a topographic compensation algorithm has been studied.To assess the algorithm’s validity and effectiveness,we applied it to P-band PolSAR datasets in four forested areas in the US.Trees in the forest stands have diverse species,and the topographic conditions of the terrain differ.Significant topographic impact on the P-band PolSAR data exists before the topographic compensation algorithm.After the algorithm,the impact decreases noticeably qualitatively and quantitatively.The algorithm is valid and effective in reducing the topographic influence on the PolSAR data and,consequently,provides a better chance of retrieving accurate forest parameters.
文摘针对传统PolSAR影像建筑区域提取方法对影像特征利用不充分、自动化程度不高的问题,研究一种基于全卷积网络(fully convolutional networks,FCN)和条件随机场(conditional random field,CRF)相结合的建筑区域提取方法。该方法充分利用FCN网络对影像进行逐像素分类并能自动提取影像高层特征的优势,首先通过制作样本集对FCN网络进行训练;然后利用训练好的模型进行初步的建筑区域提取;最后利用可以联系上下文信息的条件随机场CRF对结果进行优化处理。实验结果表明,该方法可以充分利用影像的语义信息,有效地减少孤立点,提高对细节、轮廓的提取精度,获得较高精度的建筑区域提取结果。
