Classification of multi-look polarimetric SAR data based on complex Wishart distribution

Lee, J.S.; Grunes, M.R.

doi:10.1109/ntc.1992.267879

Cited by 29 publications

(23 citation statements)

References 12 publications

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“…The results of the proposed method are compared to that of the Wishart supervised classification method [13] to verify the improvement of the proposed method on the classification. Our research is mainly about the application of the new method in urban area (as marked with red rectangle in Figure 1), thus comparisons of the classification results in urban area are shown in Figure 5.…”

Section: Comparison Between the Proposed Methods And The Wishart Supermentioning

confidence: 99%

Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area

2015

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Abstract:A novel approach is proposed for classifying the polarimetric SAR (PolSAR) data by integrating polarimetric decomposition, sub-aperture decomposition and decision tree algorithm. It is composed of three key steps: sub-aperture decomposition, feature extraction and combination, and decision tree classification. Feature extraction and combination is the main contribution to the innovation of the proposed method. Firstly, the full-resolution PolSAR image and its two sub-aperture images are decomposed to obtain the scattering entropy, average scattering angle and anisotropy, respectively. Then, the difference information between the two sub-aperture images are extracted, and combined with the target decomposition features from full-resolution images to form the classification feature set. Finally, C5.0 decision tree algorithm is used to classify the PolSAR image. A comparison between the proposed method and commonly-used Wishart supervised classification was made to verify the improvement of the proposed method on the classification. The overall accuracy using the proposed method was 88.39%, much higher than that using the Wishart supervised classification, which exhibited an overall accuracy of 69.82%. The Kappa Coefficient was 0.83, whereas that using the Wishart supervised classification was 0.56. The results indicate that the proposed method performed better than Wishart supervised classification for landscape classification in urban area using PolSAR data. Further investigation was carried out on the contribution of difference information to PolSAR classification. It was found that the sub-aperture decomposition improved the classification accuracy of forest, buildings and grassland effectively in high-density urban area. Compared with support vector machine (SVM) and QUEST OPEN ACCESSRemote Sens. 2015, 7 1381 classifier, C5.0 decision tree classifier performs more efficient in time consumption, feature selection and construction of decision rule.

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Section: Comparison Between the Proposed Methods And The Wishart Supermentioning

confidence: 99%

Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area

2015

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“…Wishart supervised classification (Lee and Grunes 1992;Lee et al 1994Lee et al , 1998Lee et al , 1999) uses a maximum likelihood classifier based on the statistical properties to perform supervised classification under the assumption of the probability density distribution function of the polarimetric covariance matrix following a complex Wishart probability distribution with n degrees of freedom, W C (n, [C]) (Lee et al 1994(Lee et al , 1999Lee and Pottier 2009 Because the double-bounce scattering power of oriented buildings is enhanced after POA compensation, the Wishart supervised classification is performed on the PolSAR data after POA compensation, which can obtain…”

Section: Wishart Supervised Classificationmentioning

confidence: 99%

Fast building damage mapping using a single post-earthquake PolSAR image: a case study of the 2010 Yushu earthquake

Zhai

Huang

2016

Earth Planet Sp

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Earthquakes are one of the most destructive natural disasters. Efficiently and quickly acquiring building earthquake damage information can help to reduce the casualties after an earthquake. In this paper, for convenience, speed, and precision, building damage information is extracted using a single post-earthquake PolSAR image. In PolSAR images, the undamaged parallel buildings characterized by double-bounce scattering are different from the collapsed buildings characterized by volume scattering, but the undamaged oriented buildings are very similar to collapsed buildings because of their scattering mechanism ambiguity in the early traditional model-based decomposition. Therefore, the collapsed buildings are difficult to extract accurately. In this paper, the scheme of polarization orientation angle (POA) compensation is employed to enhance the double-bounce scattering power of the oriented buildings, and the difference in the relative contribution change rate of scattering components before and after POA compensation is proposed to further enhance the difference between collapsed buildings and oriented buildings, in order that the collapsed buildings can be extracted more accurately. The "4.14" Yushu earthquake, which occurred in Yushu County, Qinghai province of China, is used as the case study to test the proposed method, and an airborne high-resolution PolSAR image of the urban region of Yushu County is used in the experiment. The experimental results show that the accuracy of building damage information extraction can be improved by the use of the proposed method, compared with the traditional polarimetric classification.

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“…For each of the connections, the likelihood function at site is given by (14) By omitting terms not involving the log likelihood function is given as (15) We find the MLE of the mean CM by solving , with being the 3 3 null matrix. Using expressions for the derivative of trace , with denoting inverse transpose, and the derivative of the logarithm of the determinant, [23], the MLE is found from (16) resulting in (17) From this equation it can be seen, that the locally estimated number of looks, , controls the degree of averaging.…”

Section: B ML and Map Estimates Of The Mean Cmmentioning

confidence: 99%

“…One of the most important applications of polarimetric SAR images is classification of land use areas, and several supervised and unsupervised classification schemes have been proposed in the literature [14], [15], [6], [16]. In this work, the polarimetric restoration is shown to be an efficient pre-processing step for the unsupervised classification scheme suggested by Cloude and Pottier [15].…”

Section: Introductionmentioning

confidence: 99%

Restoration of polarimetric SAR images using simulated annealing

Schou

Skriver²

2001

IEEE Trans. Geosci. Remote Sensing

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Abstract-Filtering synthethic aperture radar (SAR) images ideally results in better estimates of the parameters characterizing the distributed targets in the images while preserving the structures of the nondistributed targets. However, these objectives are normally conflicting, often leading to a filtering approach favoring one of the objectives. An algorithm for estimating the radar cross-section (RCS) for intensity SAR images has previously been proposed in the literature based on Markov random fields and the stochastic optimization method simulated annealing. A new version of the algorithm is presented applicable to multilook polarimetric SAR images, resulting in an estimate of the mean covariance matrix rather than the RCS. Small windows are applied in the filtering, and due to the iterative nature of the approach, reasonable estimates of the polarimetric quantities characterizing the distributed targets are obtained while at the same time preserving most of the structures in the image. The algorithm is evaluated using multilook polarimetric L-band data from the Danish airborne EMISAR system, and the impact of the algorithm on the unsupervised -classification is demonstrated.

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Classification of multi-look polarimetric SAR data based on complex Wishart distribution

Cited by 29 publications

References 12 publications

Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area

Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area

Fast building damage mapping using a single post-earthquake PolSAR image: a case study of the 2010 Yushu earthquake

Restoration of polarimetric SAR images using simulated annealing

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