A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of ...A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.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
Speckle effects on classification results can be sup- pressed to some extent by introducing the contextual information. An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar （...Speckle effects on classification results can be sup- pressed to some extent by introducing the contextual information. An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar （POLSAR） images based on the mean shift （MS） segmentation and Markov random field （MRF）. First, polarimetdc features are exacted by target decomposition for MS segmentation. An initial classification is executed by using the target decomposition and the agglomerative hierarchical clus- tering algorithm. Thereafter, a classification step based on MRF is performed by using the mean coherence matrices obtained for each segment. Under the MRF framework, the smoothness term is defined according to the distance between neighboring areas. By using POLSAR images acquired by the German Aerospace Centre and National Aeronautics and Space Administration/Jet Propulsion Laboratory, the experimental results confirm that the proposed method has higher accuracy and better regional connectivity than other classification methods.展开更多
基金Supported by the National Natural Science Foundation of China(62376214)the Natural Science Basic Research Program of Shaanxi(2023-JC-YB-533)Foundation of Ministry of Education Key Lab.of Cognitive Radio and Information Processing(Guilin University of Electronic Technology)(CRKL200203)。
文摘A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.
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。
基金Supported by National Basic Research Development Program of China（973 Program）（2007CB311006） National Natural Science Foundation of China（60602026）,Acknowledgement The authors would like to thank ESA （http：//earth.esa. int/polsarpro/datasets.html） for providing the data.
基金supported by the National Natural Science Foundation of China(6100118741001256+1 种基金40971219)the National High Technology Research and Development Program of China(863 Program)(2013 AA122301)
文摘Speckle effects on classification results can be sup- pressed to some extent by introducing the contextual information. An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar （POLSAR） images based on the mean shift （MS） segmentation and Markov random field （MRF）. First, polarimetdc features are exacted by target decomposition for MS segmentation. An initial classification is executed by using the target decomposition and the agglomerative hierarchical clus- tering algorithm. Thereafter, a classification step based on MRF is performed by using the mean coherence matrices obtained for each segment. Under the MRF framework, the smoothness term is defined according to the distance between neighboring areas. By using POLSAR images acquired by the German Aerospace Centre and National Aeronautics and Space Administration/Jet Propulsion Laboratory, the experimental results confirm that the proposed method has higher accuracy and better regional connectivity than other classification methods.
