Region-Based Classification of Polarimetric SAR Images Using Wishart MRF

Wu, Yiwen; Ji, Kefeng; Wang, Yu; Su, Yi

doi:10.1109/lgrs.2008.2002263

Cited by 150 publications

(101 citation statements)

References 12 publications

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“…2016, 8, 619 3 of 22 Wu et al [23] incorporated the Wishart distribution with MRF for PolSAR image classification. Akbari et al [6] proposed an unsupervised classification method based on the K distribution and MRF.…”

Section: Related Workmentioning

confidence: 99%

“…However, several drawbacks in the traditional pixel-based classification still exist due to inherent speckle noise [23,27,29,30]. By contrast, object-oriented classification offers a promising scheme.…”

Section: Related Workmentioning

confidence: 99%

“…The experimental image with the size of 380 pixels × 424 pixels is shown in Figure 7a. The ground truth map is taken from [23,32] and is shown in Figure 7b. According to the ground truth map, seven classes of crops and one class of bare soil are present in the experimental image.…”

Section: Description Of the Experimental Datasetsmentioning

confidence: 99%

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Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

Chen

Yang

et al. 2016

Remote Sensing

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Classification techniques play an important role in the analysis of polarimetric synthetic aperture radar (PolSAR) images. PolSAR image classification is widely used in the fields of information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, inherent speckle noise of PolSAR images hinders its application on further classification. A novel supervised superpixel-based classification method is proposed in this study to suppress the influence of speckle noise on PolSAR images for the purpose of obtaining accurate and consistent classification results. This method combines statistical information with spatial context information based on the stochastic expectation maximization (SEM) algorithm. First, a modified simple linear iterative clustering (SLIC) algorithm is utilized to generate superpixels as classification elements. Second, class posterior probabilities of superpixels are calculated by a K distribution in iterations of SEM. Then, a neighborhood function is defined to express the spatial relationship among adjacent superpixels quantitatively, and the class posterior probabilities are updated by this predefined neighborhood function in a probabilistic label relaxation (PLR) procedure. The final classification result is obtained by the maximum a posteriori decision rule. A simulated image, a spaceborne RADARSAT-2 image, and an airborne AIRSAR image are used to evaluate the proposed method, and the classification accuracy of our proposed method is 99.28%, 93.16% and 89.70%, respectively. The experimental results indicate that the proposed method obtains more accurate and consistent results than other methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

Chen

Yang

et al. 2016

Remote Sensing

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“…The class label to a region is determined collectively by all the pixels in that region. As an example, Wu [10] proposed such a method that combines the Wishart distribution with the Markov random field (MRF). Although additional information of spatial correlations may improve classification accuracy, it also introduces extra complexity in modeling and computation.…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification for Polarimetric SAR Images Based on Mixture Models

Gao

Yang

2014

Remote Sensing

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In this paper, two mixture models are proposed for modeling heterogeneous regions in single-look and multi-look polarimetric SAR images, along with their corresponding maximum likelihood classifiers for land cover classification. The classical Gaussian and Wishart models are suitable for modeling scattering vectors and covariance matrices from homogeneous regions, while their performance deteriorates for regions that are heterogeneous. By comparison, the proposed mixture models reduce the modeling error by expressing the data distribution as a weighted sum of multiple component distributions. For single-look and multi-look polarimetric SAR data, complex Gaussian and complex Wishart components are adopted, respectively. Model parameters are determined by employing the expectation-maximization (EM) algorithm. Two maximum likelihood classifiers are then constructed based on the proposed mixture models. These classifiers are assessed using polarimetric SAR images from the RADARSAT-2 sensor of the Canadian Space Agency (CSA), the AIRSAR sensor of the Jet Propulsion Laboratory (JPL) and the EMISAR sensor of the Technical University of Denmark (DTU). Experiment results demonstrate that the new models fit heterogeneous regions preferably to the classical models and are especially appropriate for extremely heterogeneous regions, such as urban areas. The overall accuracy of land cover classification is also improved due to the more refined modeling.

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“…Over the past decade, there has been extensive research in the area of the segmentation and classification of polarimetric SAR data. In the literature, the classification algorithms for polarimetric SAR can be divided into three main classes: 1) classification based on physical scattering mechanisms inherent in data [1,2], 2) classification based on statistical characteristics of data [3,4] and 3) classification based on image processing techniques [5,6]. Additionally, there has been several works using some combinations of the above classification approaches [3,1].…”

Section: Introductionmentioning

confidence: 99%

Polarimetric SAR Image Classification Using Radial Basis Function Neural Network

İnce¹

2010

PIERS Online

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Abstract-This paper presents a robust radial basis function (RBF) network based classifier for polarimetric synthetic aperture radar (SAR) images. The proposed feature extraction process utilizes the covariance matrix, the gray level co-occurrence matrix (GLCM) based texture features, and the backscattering power (Span) combined with the H/α/A decomposition, which are projected onto a lower dimensional feature space using principal component analysis. For the classifier training two popular techniques are explored: conventional backpropagation (BP) and particle swarm optimization (PSO). By using both polarimetric covariance matrix and decomposition based pixel values and textural information (contrast, correlation, energy, and homogeneity) in the feature set, classification accuracy is improved. An experimental study is performed using the fully polarimetric San Francisco Bay and Flevoland data sets acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared with competing state of the art algorithms demonstrate the effectiveness of the proposed RBF network classifier.

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Region-Based Classification of Polarimetric SAR Images Using Wishart MRF

Cited by 150 publications

References 12 publications

Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

Land Cover Classification for Polarimetric SAR Images Based on Mixture Models

Polarimetric SAR Image Classification Using Radial Basis Function Neural Network

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