Results 131 to 140 of about 840 (174)
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The Influence of Distances in NLM Polsar Filters
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 20195112
Luís Gómez Déniz, Alejandro C. Frery
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A new change detector in PolSAR imagery
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Change detection is an important issue in many applications. In this study, we propose a new change detector for multi-temporal polarimetric synthetic aperture radar (SAR) images. The new detector is based on the optimization of polarimetric contrast minimization. The optimized solution of the contrast model is also given.
Junjun Yin 0001, Jian Yang 0011
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A four-component decomposition of POLSAR image
Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., 2005Abstract : A four-component scattering model is proposed to decompose polarimetric synthetic aperture radar images. The covariance matrix approach is used to deal with the non-reflection symmetric scattering case. This scheme includes and extends the three-component decomposition method dealing with the reflection symmetry condition that the co-pol and
Yoshio Yamaguchi +3 more
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Assessment of Model-Based Polsar Decompositions
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Model-based polarimetric decompositions are often used to generate scene classifications from polarimetric SAR imagery. The original Freeman-Durden model has been modified and improved upon multiple times over the past 2 decades. However, quantitative, in-depth analyses of these incoherent model-based decompositions have lagged in comparison.
Thomas L. Ainsworth +2 more
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Analysis of non-Gaussian POLSAR data
2007 IEEE International Geoscience and Remote Sensing Symposium, 2007In this paper we present a generalised Wishart classifier derived from a non-Gaussian model for polarimetric synthetic aperture radar (POLSAR) data. Our starting point is to demonstrate that the scale mixture of Gaussian (SMoG) distribution model is suitable for modelling POLSAR data.
Anthony Paul Doulgeris +2 more
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Evaluation of polsar similarity measures with spectral clustering
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017Polarimetric Synthetic Aperture Radar (PolSAR) is a valuable remote sensing data source. It is usually challenging to interpret PolSAR data, especially in urban areas, and hense, spatial clustering comes as a powerful tool for the application of PolSAR data. In data clustering, similarity measurement indexes are of great importance.
Hu, Jingliang +3 more
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Polsar Terrain Classification Based on Denoising-CNN
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Terrain classification plays an important role in understanding Polari- metric Synthetic Aperture Radar (PolSAR) image intuitively. In the process of classification, feature extraction is critical. However, the preprocess of speckle noise filtering affects the effectiveness of the feature extractor which influences the accuracy of classification ...
Yanhe Guo +5 more
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River Ice Mapping from PolSAR Images
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008This paper presents different mapping algorithms to discriminate river ice types using full-polarized C-band and dual-polarized X-band data. Field data are conjointly used with an electromagnetic river ice model to simulate backscattering response of river ice. Finally different classifications, proposed and tested, show encouraging results.
Stephane Mermoz +3 more
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Classification With a Non-Gaussian Model for PolSAR Data
IEEE Transactions on Geoscience and Remote Sensing, 2008In this paper, we present a generalized Wishart classifier derived from a non-Gaussian model for polarimetric synthetic aperture radar (PolSAR) data. Our starting point is to demonstrate that the scale mixture of Gaussian (SMoG) distribution model is suitable for modeling PolSAR data.
Anthony Paul Doulgeris +2 more
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Riemannian sparse coding for classification of PolSAR images
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Hermitian positive definite (HPD) covariance matrices form one of the most widely-used data representations in PolSAR applications. However, most of these applications either use statistical distribution models on the PolSAR covariance matrices or polarimetric target decomposition. In this paper, we study HPD matrices for PolSAR image classification in
Wen Yang 0001 +3 more
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