The Experts below are selected from a list of 12027 Experts worldwide ranked by ideXlab platform
S. Sista - One of the best experts on this subject based on the ideXlab platform.
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Bayesian framework for Unsupervised Classification with application to target tracking
1999 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999Co-Authors: R.l. Kashyap, S. SistaAbstract:We have given a solution to the problem of Unsupervised Classification of multidimensional data. Our approach is based on Bayesian estimation which regards the number of classes, the data partition and the parameter vectors that describe the density of classes as unknowns. We compute their MAP estimates simultaneously by maximizing their joint posterior probability density given the data. The concept of partition as a variable to be estimated is a unique feature of our method. This formulation also solves the problem of validating clusters obtained from various methods. Our method can also incorporate any additional information about a class while assigning its probability density. It can also utilize any available training samples that arise from different classes. We provide a descent algorithm that starts with an arbitrary partition of the data and iteratively computes the MAP estimates. The proposed method is applied to target tracking data. The results obtained demonstrate the power of the Bayesian approach for Unsupervised Classification.
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ICASSP - Bayesian framework for Unsupervised Classification with application to target tracking
1999 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999Co-Authors: R.l. Kashyap, S. SistaAbstract:We have given a solution to the problem of Unsupervised Classification of multidimensional data. Our approach is based on Bayesian estimation which regards the number of classes, the data partition and the parameter vectors that describe the density of classes as unknowns. We compute their MAP estimates simultaneously by maximizing their joint posterior probability density given the data. The concept of partition as a variable to be estimated is a unique feature of our method. This formulation also solves the problem of validating clusters obtained from various methods. Our method can also incorporate any additional information about a class while assigning its probability density. It can also utilize any available training samples that arise from different classes. We provide a descent algorithm that starts with an arbitrary partition of the data and iteratively computes the MAP estimates. The proposed method is applied to target tracking data. The results obtained demonstrate the power of the Bayesian approach for Unsupervised Classification.
R.l. Kashyap - One of the best experts on this subject based on the ideXlab platform.
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Bayesian framework for Unsupervised Classification with application to target tracking
1999 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999Co-Authors: R.l. Kashyap, S. SistaAbstract:We have given a solution to the problem of Unsupervised Classification of multidimensional data. Our approach is based on Bayesian estimation which regards the number of classes, the data partition and the parameter vectors that describe the density of classes as unknowns. We compute their MAP estimates simultaneously by maximizing their joint posterior probability density given the data. The concept of partition as a variable to be estimated is a unique feature of our method. This formulation also solves the problem of validating clusters obtained from various methods. Our method can also incorporate any additional information about a class while assigning its probability density. It can also utilize any available training samples that arise from different classes. We provide a descent algorithm that starts with an arbitrary partition of the data and iteratively computes the MAP estimates. The proposed method is applied to target tracking data. The results obtained demonstrate the power of the Bayesian approach for Unsupervised Classification.
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ICASSP - Bayesian framework for Unsupervised Classification with application to target tracking
1999 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999Co-Authors: R.l. Kashyap, S. SistaAbstract:We have given a solution to the problem of Unsupervised Classification of multidimensional data. Our approach is based on Bayesian estimation which regards the number of classes, the data partition and the parameter vectors that describe the density of classes as unknowns. We compute their MAP estimates simultaneously by maximizing their joint posterior probability density given the data. The concept of partition as a variable to be estimated is a unique feature of our method. This formulation also solves the problem of validating clusters obtained from various methods. Our method can also incorporate any additional information about a class while assigning its probability density. It can also utilize any available training samples that arise from different classes. We provide a descent algorithm that starts with an arbitrary partition of the data and iteratively computes the MAP estimates. The proposed method is applied to target tracking data. The results obtained demonstrate the power of the Bayesian approach for Unsupervised Classification.
Dmitri N. Moisseev - One of the best experts on this subject based on the ideXlab platform.
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Unsupervised Classification of vertical profiles of dual polarization radar variables
Atmospheric Measurement Techniques, 2020Co-Authors: Jussi Tiira, Dmitri N. MoisseevAbstract:Abstract. Vertical profiles of polarimetric radar variables can be used to identify fingerprints of snow growth processes. In order to systematically study such manifestations of precipitation processes, we have developed an Unsupervised Classification method. The method is based on k -means clustering of vertical profiles of polarimetric radar variables, namely reflectivity, differential reflectivity and specific differential phase. For rain events, the Classification is applied to radar profiles truncated at the melting layer top. For the snowfall cases, the surface air temperature is used as an additional input parameter. The proposed Unsupervised Classification was applied to 3.5 years of data collected by the Finnish Meteorological Institute Ikaalinen radar. The vertical profiles of radar variables were computed above the University of Helsinki Hyytiala station, located 64 km east of the radar. Using these data, we show that the profiles of radar variables can be grouped into 10 and 16 classes for rainfall and snowfall events, respectively. These classes seem to capture most important snow growth and ice cloud processes. Using this Classification, the main features of the precipitation formation processes, as observed in Finland, are presented.
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Unsupervised Classification of vertical profiles of dual polarization radar variables
2019Co-Authors: Jussi Tiira, Dmitri N. MoisseevAbstract:Abstract. Vertical profiles of polarimetric radar variables can be used to identify fingerprints of snow growth processes. In order to systematically study such manifestations of precipitation processes, we have developed an Unsupervised Classification method. The method is based on k-means clustering of vertical profiles of polarimetric radar variables, namely reflectivity, differential reflectivity and specific differential phase. For rain events, the Classification is applied to radar profiles truncated at the melting layer top. For the snowfall cases, the surface air temperature is used as an additional input parameter. The proposed Unsupervised Classification was applied to 3.5 years of data collected by the Finnish Meteorological Institute Ikaalinen radar. The vertical profiles of radar variables were computed above the University of Helsinki Hyytiälä station, located 64 km east of the radar. Using these data, we show that the profiles of radar variables can be grouped into 10 and 16 classes for rainfall and snowfall events respectively. These classes seem to capture most important snow growth and ice cloud processes. Using this Classification, main features of the precipitation formation processes, as observed in Finland, are presented.
Wen Yang - One of the best experts on this subject based on the ideXlab platform.
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Unsupervised Classification of Polarimetric SAR Images via Riemannian Sparse Coding
IEEE Transactions on Geoscience and Remote Sensing, 2017Co-Authors: Neng Zhong, Wen Yang, Anoop Cherian, Xiangli Yang, Mingsheng LiaoAbstract:Unsupervised Classification plays an important role in understanding polarimetric synthetic aperture radar (PolSAR) images. One of the typical representations of PolSAR data is in the form of Hermitian positive definite (HPD) covariance matrices. Most algorithms for Unsupervised Classification using this representation either use statistical distribution models or adopt polarimetric target decompositions. In this paper, we propose an Unsupervised Classification method by introducing a sparsity-based similarity measure on HPD matrices. Specifically, we first use a novel Riemannian sparse coding scheme for representing each HPD covariance matrix as sparse linear combinations of other HPD matrices, where the sparse reconstruction loss is defined by the Riemannian geodesic distance between HPD matrices. The coefficient vectors generated by this step reflect the neighborhood structure of HPD matrices embedded in the Euclidean space and hence can be used to define a similarity measure. We apply the scheme for PolSAR data, in which we first oversegment the images into superpixels, followed by representing each superpixel by an HPD matrix. These HPD matrices are then sparse coded, and the resulting sparse coefficient vectors are then clustered by spectral clustering using the neighborhood matrix generated by our similarity measure. The experimental results on different fully PolSAR images demonstrate the superior performance of the proposed Classification approach against the state-of-the-art approaches.
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Fusion of intensity/coherent information using region covariance features for Unsupervised Classification of SAR imagery
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Xiangli Yang, Shangtan Tu, Wen YangAbstract:Unsupervised Classification of synthetic aperture radar (SAR) imagery is an essential step in SAR image interpretation. There is a growing demand for an efficient way to fuse multi-information of SAR imagery. This paper presents an intensity/coherent information fusion algorithm by using region covariance features for Unsupervised Classification. More precisely, we firstly extract the intensity properties and coherent characteristics from each pixel of SAR imagery, then use the region covariance descriptor to fuse the intensity and coherent features, and finally exploit the K-means algorithm to obtain the final Unsupervised Classification map. Experimental results on SAR imagery demonstrate the effectiveness of the proposed fusion scheme.
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IGARSS - Fusion of intensity/coherent information using region covariance features for Unsupervised Classification of SAR imagery
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Xiangli Yang, Shangtan Tu, Wen YangAbstract:Unsupervised Classification of synthetic aperture radar (SAR) imagery is an essential step in SAR image interpretation. There is a growing demand for an efficient way to fuse multi-information of SAR imagery. This paper presents an intensity/coherent information fusion algorithm by using region covariance features for Unsupervised Classification. More precisely, we firstly extract the intensity properties and coherent characteristics from each pixel of SAR imagery, then use the region covariance descriptor to fuse the intensity and coherent features, and finally exploit the K-means algorithm to obtain the final Unsupervised Classification map. Experimental results on SAR imagery demonstrate the effectiveness of the proposed fusion scheme.
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Unsupervised Classification of PolInSAR image based on Shannon Entropy Characterization
IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 2010Co-Authors: Wen YangAbstract:In this paper, we propose a new method for Unsupervised Classification of polarimetric synthetic aperture radar interferometry (PolInSAR) images based on Shannon Entropy Characterization. Firstly, we use polarimetric H (entropy) and a parameters to classify the image initially. Then, we reclassify the image according to the span of Shannon Entropy Characterization. Finally, we fuse the results of the two previous steps and merge them to the specified number of clusters. The effectiveness of this method is demonstrated on CETC38 PolInSAR data and E-SAR PolInSAR data.
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Improved Unsupervised Classification Based on Freeman-Durden Polarimetric Decomposition
7th European Conference on Synthetic Aperture Radar, 2008Co-Authors: Wen Yang, Tong Yuan Zhou, Hong SunAbstract:An improved Unsupervised Classification algorithm based on Freeman-Durden decomposition is presented. We investigate four different combinations of three basic scattering mechanisms through introducing the scattering power entropy and anisotropy parameter.Classificaiton result on NASA/JPL AIRSAR L-band POLSAR data demonstrates the effectiveness of our algorithm.
S.r. Cloude - One of the best experts on this subject based on the ideXlab platform.
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Unsupervised Classification using polarimetric decomposition and the complex Wishart classifier
IEEE Transactions on Geoscience and Remote Sensing, 1999Co-Authors: T.l. Ainsworth, D.l. Schuler, Li-jen Du, S.r. CloudeAbstract:The authors propose a new method for Unsupervised Classification of terrain types and man-made objects using polarimetric synthetic aperture radar (SAR) data. This technique is a combination of the Unsupervised Classification based on polarimetric target decomposition, S.R. Cloude et al. (1997), and the maximum likelihood classifier based on the complex Wishart distribution for the polarimetric covariance matrix, J.S. Lee et al. (1994). The authors use Cloude and Pottier's method to initially classify the polarimetric SAR image. The initial Classification map defines training sets for Classification based on the Wishart distribution. The classified results are then used to define training sets for the next iteration. Significant improvement has been observed in iteration. The iteration ends when the number of pixels switching classes becomes smaller than a predetermined number or when other criteria are met. The authors observed that the class centers in the entropy-alpha plane are shifted by each iteration. The final class centers in the entropy-alpha plane are useful for class identification by the scattering mechanism associated with each zone. The advantages of this method are the automated Classification, and the interpretation of each class based on scattering mechanism. The effectiveness of this algorithm is demonstrated using a JPL/AIRSAR polarimetric SAR image.
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Unsupervised Classification using polarimetric decomposition and complex Wishart classifier
IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174), 1998Co-Authors: T.l. Ainsworth, L. Du, D.l. Schuler, S.r. CloudeAbstract:The authors propose a new method for Unsupervised Classification of terrain types and man-made objects using polarimetric SAR data. This technique is a combination of the Unsupervised Classification based on the polarimetric target decomposition (Cloude and Pottier, 1997) and the maximum likelihood classifier based on the complex Wishart distribution (Lee et al., 1994). The advantage of this approach is that clusters may be identified by the scattering mechanisms from the target decomposition. The effectiveness of this algorithm is demonstrated using JPL/AIRSAR and SIR-C polarimetric SAR images.
