In this study,we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea.A bistatic SAR scene acquired by the TanDEM-X mission over t...In this study,we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea.A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis.Backscatter intensity,interferometric coherence magnitude,and interferometric phase have been used as informative features in several classification experiments.Various combinations of classification features were evaluated using Maximum likelihood(ML),Random Forests(RF)and Support Vector Machine(SVM)classifiers to achieve the best possible discrimination between open water and several sea ice types(undeformed ice,ridged ice,moderately deformed ice,brash ice,thick level ice,and new ice).Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification per-formance compared to using only backscatter-intensity.The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies,however,at the expense of somewhat longer processing time.The best overall accuracy(OA)for three methodologies were achieved using combination of all tested features were 71.56,72.93,and 72.91%for ML,RF and SVM classifiers,respectively.Compared to OAs of 62.28,66.51,and 63.05%using only backscatter intensity,this indicates strong benefit of SAR interferometry in discriminating different types of sea ice.In contrast to several earlier studies,we were particularly able to successfully discriminate open water and new ice classes.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
最大平均相关高度(MACH:Maximum Average Correlation Height)滤波器是一种重要的基于相关的模式识别方法。滤波器由训练数据线性构造而成,具有良好的畸变容忍能力,在线性高斯噪声条件下具有理论最优性。为将算法适用于广泛的非线性、...最大平均相关高度(MACH:Maximum Average Correlation Height)滤波器是一种重要的基于相关的模式识别方法。滤波器由训练数据线性构造而成,具有良好的畸变容忍能力,在线性高斯噪声条件下具有理论最优性。为将算法适用于广泛的非线性、非高斯情形,本文引入一种新的度量函数相关熵,可隐性地将输入数据通过非线性变换映射到特征空间;并在新的空间中提出了基于相关熵的MACH滤波器构造方法。最后将此方法应用于合成孔径雷达(SAR:Synthetic Aperture Radar)图像目标分类进行了实验,在接收机工作性能曲线和峰值旁瓣比的比对中,本文算法的性能均有所提升。展开更多
The orthogonal signals of multi-carrier-frequency emission and multiple antennas receipt module are used in SIAR radar.The corresponding received echo is equivalent to non-uniform spatial sampling after the frequency ...The orthogonal signals of multi-carrier-frequency emission and multiple antennas receipt module are used in SIAR radar.The corresponding received echo is equivalent to non-uniform spatial sampling after the frequency diversity process.As using the traditional Fourier transform will result in the target spectral with large sidelobe,the method presented in this paper firstly makes the preordering treatment for the position of the received antenna.Then,the Bayesian maximum posteriori estimation with l2-norm weighted constraint is utilized to achieve the equivalent uniform array echo.The simulations present the spectrum estimation in angle precision estimation of multiple targets under different SNRs,different virtual antenna numbers and different elevations.The estimation results confirm the advantage of SIAR radar both in array expansion and angle estimation.展开更多
Synthetic aperture radar（SAR）image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld（DAMRF...Synthetic aperture radar（SAR）image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld（DAMRF）and maximum a posteriori（MAP）estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly developed.In the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method.Four normalized ratios constructed in different directions are computed,based on which two edge strength maps（ESMs）are formed.The fnal edge detection result is achieved by fusing the results of two thresholded ESMs.The experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt＇s fgure of merit and visual evaluation in most cases.展开更多
Synthetic aperture radar （SAR） imagery is a kind of coherent system that produces a random pattern, named speckle, which degrades the merit of SAR images and affects their further application seriously. Therefore, h...Synthetic aperture radar （SAR） imagery is a kind of coherent system that produces a random pattern, named speckle, which degrades the merit of SAR images and affects their further application seriously. Therefore, how to restore SAR image from the speckle has become a necessary step in post-processing of image. A new despeckling method is putforth on the basis of wavelet. First, a new approach on the basis of ＂second kind statistics＂ is used to estimate the dispersion parameter of the Cauchy distribution. Then, this Cauchy prior is applied to model the distribution of the wavelet coefficients for the log-transformed reflectance of SAR image. Based on the above ideas, a new homomorphic wavelet-based maximum a posterior （MAP） despeckling method is proposed. Finally, the simulated speckled image and the real SAR image are used to verify our proposed method and the results show that it outperforms the other methods in terms of the speckle reduction and the feature retention.展开更多
基金This research was supported by Academy of Finland under Grant no.296628.
文摘In this study,we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea.A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis.Backscatter intensity,interferometric coherence magnitude,and interferometric phase have been used as informative features in several classification experiments.Various combinations of classification features were evaluated using Maximum likelihood(ML),Random Forests(RF)and Support Vector Machine(SVM)classifiers to achieve the best possible discrimination between open water and several sea ice types(undeformed ice,ridged ice,moderately deformed ice,brash ice,thick level ice,and new ice).Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification per-formance compared to using only backscatter-intensity.The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies,however,at the expense of somewhat longer processing time.The best overall accuracy(OA)for three methodologies were achieved using combination of all tested features were 71.56,72.93,and 72.91%for ML,RF and SVM classifiers,respectively.Compared to OAs of 62.28,66.51,and 63.05%using only backscatter intensity,this indicates strong benefit of SAR interferometry in discriminating different types of sea ice.In contrast to several earlier studies,we were particularly able to successfully discriminate open water and new ice classes.
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.
基金supported by the Specialized Research Fund for the Doc-toral Program of Higher Education (Grant No. 200807010004)the National Natural Science Foundation of China (Grant Nos. 60776795, 60902079, 60902031), the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) (Grant No. IRT0645)
文摘The orthogonal signals of multi-carrier-frequency emission and multiple antennas receipt module are used in SIAR radar.The corresponding received echo is equivalent to non-uniform spatial sampling after the frequency diversity process.As using the traditional Fourier transform will result in the target spectral with large sidelobe,the method presented in this paper firstly makes the preordering treatment for the position of the received antenna.Then,the Bayesian maximum posteriori estimation with l2-norm weighted constraint is utilized to achieve the equivalent uniform array echo.The simulations present the spectrum estimation in angle precision estimation of multiple targets under different SNRs,different virtual antenna numbers and different elevations.The estimation results confirm the advantage of SIAR radar both in array expansion and angle estimation.
基金supported National Natural Science Foundation of China (No.61102167)
文摘Synthetic aperture radar（SAR）image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld（DAMRF）and maximum a posteriori（MAP）estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly developed.In the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method.Four normalized ratios constructed in different directions are computed,based on which two edge strength maps（ESMs）are formed.The fnal edge detection result is achieved by fusing the results of two thresholded ESMs.The experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt＇s fgure of merit and visual evaluation in most cases.
文摘Synthetic aperture radar （SAR） imagery is a kind of coherent system that produces a random pattern, named speckle, which degrades the merit of SAR images and affects their further application seriously. Therefore, how to restore SAR image from the speckle has become a necessary step in post-processing of image. A new despeckling method is putforth on the basis of wavelet. First, a new approach on the basis of ＂second kind statistics＂ is used to estimate the dispersion parameter of the Cauchy distribution. Then, this Cauchy prior is applied to model the distribution of the wavelet coefficients for the log-transformed reflectance of SAR image. Based on the above ideas, a new homomorphic wavelet-based maximum a posterior （MAP） despeckling method is proposed. Finally, the simulated speckled image and the real SAR image are used to verify our proposed method and the results show that it outperforms the other methods in terms of the speckle reduction and the feature retention.
