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Supervised classification of hyperspectral images is a challenging task due to the relatively low ratio between the number of training samples and the number of spectral channels. Subspace-based classification methods deal with this difficulty by assuming that feature vectors lie in a low-dimensional subspace. Based on the fact that a class in a hyperspectral image may be composed of a number of different...
Multi-aspect PolSAR data contains polarimetric properties from different look angle. Multi-aspect polarimetric information can be applied in geometric measurement, target identifying, precise classification. In order to characterize anisotropic target, anisotropic and isotropic scattering need to be separated from the raw data. A detecting-removing-incoherent-adding (DRIA) framework, presented in...
The increasing spectrum occupancy is a serious threat for highly sensitive astronomical measurement at radio frequencies. The deployment of array radio telescopes allows the exploitation of spatial information regarding sources of radio frequency interference (RFI) in order to filter them out of astronomical data and theoretical recover uncorrupted time and frequency data. This paper introduces the...
A three-component decomposition algorithm is proposed for polarimetric SAR data. After extracting the volume scattering component, both the orientation angle compensation and a unitary transformation are applied to the remaining matrix to derive the second and third components which are exactly consistent with either the surface scattering model or the double-bounce scattering model, respectively...
The log-cumulants of the second and third order are widely used to determine the statistical model of PolSAR data. However, same values of these statistics could result from both the product model and the mixture model, which represent two different physical scenarios. In other words, there is an ambiguity between the texture and the mixture according to these statistics. In this work, the log-cumulant...
The aim of this study is to build a model suitable to classify grassland management practices using satellite image time series with high spatial resolution. The study site is located in southern France where 52 parcels with three management types were selected. The NDVI computed from a Formosat-2 intra-annual time series of 17 images was used. To work at the parcel scale while accounting for the...
In this paper,we propose a sparse Baysesian learning (SBL) based approach for the DOA estimation in the presence of coherent sources. First, the difference technique is used to enhance the input SNR. Then, we construct a virtual array manifold to eliminate the cross-term effect between each coherent group, after eigenvalue decomposition (EVD) of the difference covariance matrix, reduce the dimension...
Polarimetric synthetic aperture radar (PolSAR) images are widely applied in terrain and ground cover classification. Feature extraction and classifier design are both important in Pol- SAR image classification. In this paper, various target decompositions are applied to obtain different polarimetric features. Since that neighboring pixels usually belong to the same species, they can be simultaneously...
The ground-volume separation of radar scattering plays an important role in the analysis of forested scenes. For this purpose, the data covariance matrix of multi-polarimetric (MP) multi-baseline (MB) SAR surveys can be represented thru a sum of two Kronecker products composed of the data covariance matrices and polarimetric signatures that correspond to the ground and canopy scattering mechanisms...
Downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain 3-D scene properties and has broad application prospects. However, the reconstruction of cross-track dimension usually suffers from incomplete observation, which is caused by the non-uniformly and sparsely distributed virtual antenna phase centers. By formulating the cross-track reconstruction...
Entropy, alpha, and anisotropy (H/ᾱ/A) of Cloude decomposition are effective in polarimetric SAR image understanding and geophysical information inversion. As an incoherent target decomposition, the inner sample covariance matrix estimation severely affects the estimated parameters. The contradiction between details preservation and accurate parameters estimation is still a challenge task. In this...
In statistical classification, such mixture models allow a formal approach to unsupervised clustering. Fitting mixture distributions can be handled by a wide variety of techniques. A standard method to fit finite mixture models to observed data is the Expectation-Maximization (EM) algorithm which is an iterative procedure which converges to a (local) maximum of the marginal a posteriori probability...
In this paper, we present a new approach to solve the problem of volume scattering ambiguity in urban area, for that we propose a volume model on the correlation coefficient of pauli component (HH-VV) using polarimetric sar interrferometry PolInSAR data. The new model is more adaptive and fits better with both forest and oriented builtup areas. Thereby, a new model-based polarimetric decomposition...
The most popular model-based decompositions (MBD) are reconsidered in the context of the estimation theory. It is shown that a large processing window is required to reduce the bias on the individual scattering contribution due the target scattering Reflection symmetry assumption. This limits the MBD efficiency in areas of non stationarity radar backscattering, such as urban areas. Eigenvector-based...
Hermitian 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 the context of sparse coding. More specifically,...
In this paper, we propose a fast PolSAR image superpixel segmentation method. This method takes a simple coarse-to-fine optimization technique to minimize a Markov-Random-Field (MRF) like energy function which integrates the Pol- SAR image statistic, spatial position and boundary smoothing. It updates boundary of superpixels staring with a large block level and iterates down to the final pixel level...
An one-dimensional variational retrieval system was developed to retrieve the clear sky atmospheric temperature and humidity profiles over land using the measurements of microwave humidity-temperature sounder (MWHTS) on Chinese FY-3C satellite. The system parameters are configured by analyzing the MWHTS channel properties and the climate condition over land. The retrieval results are evaluated by...
Based on the complex Wishart distribution, statistical analysis of sea clutter acquired by quad-pol coherent X-band marine radar is presented. In this work, the probability density distribution functions (PDFs) of the intensity as well as the phase, the real and the imaginary parts of the elements in the polarimetric covariance matrix have been derived, and the theoretical models are expressed in...
Polarimetric incoherent target decomposition aims in accessing physical parameters of illuminated scatters through the analysis of target coherence or covariance matrix. In this framework, Independent Component Analysis (ICA) was recently proposed as an alternative method to Eigenvector decomposition to better interpret non-Gaussian heterogeneous clutter (inherent to high resolution SAR systems)....
Cloude-Pottier incoherent target decomposition (ICTD) and Touzi ICTD has been widely applied as a popular approach to interpret the scattering characteristics of a target in polarimetric synthetic aperture radar (PolSAR) data processing. However, they have a common drawback, i.e. proliferation of parameters (PoP) is unavoidable. Paladini et al. solved this problem by developing an orientation-invariant...
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