The Experts below are selected from a list of 42762 Experts worldwide ranked by ideXlab platform
Pierre Borgnat - One of the best experts on this subject based on the ideXlab platform.
-
A Nonparametric Test for slowly-varying nonstationarities
Signal Processing, 2018Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity, atmospheric effects) and instrumental configurations (e.g., sensor noise), as well as material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.
-
a Nonparametric Test for slowly varying nonstationarities
Signal Processing, 2018Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:Abstract This paper develops a new Nonparametric method that is suitable for detecting slowly-varying nonstationarities that can be seen as trends in the time marginal of the time-varying spectrum of the signal. The rationale behind the proposed method is to measure the importance of the trend in the time marginal by using a proper Test statistic, and further compare this measurement with the ones that are likely to be found in stationary references. It is shown that the distribution of the Test statistic under stationarity can be modeled fairly well by a Generalized Extreme Value (GEV) pdf, from which a threshold can be derived for Testing stationarity by means of a hypothesis Test. Finally, the new method is compared with other ones found in the literature.
-
A new Nonparametric method for Testing stationarity based on trend analysis in the time marginal distribution
2014Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:In this manuscript, we propose a novel Nonparametric Test for nonstationarities that are seen as a trend or an evolution in the local energy of the signal. The idea of the proposed technique consists in applying empirical mode decomposition for estimating and further quantifying the trend in the time marginal of the estimated time-frequency representation. Such methodology allows for the detection of slowly-varying nonstationarities of first and second-order.
Jocelyn Chanussot - One of the best experts on this subject based on the ideXlab platform.
-
A Nonparametric Test for slowly-varying nonstationarities
Signal Processing, 2018Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity, atmospheric effects) and instrumental configurations (e.g., sensor noise), as well as material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.
-
a Nonparametric Test for slowly varying nonstationarities
Signal Processing, 2018Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:Abstract This paper develops a new Nonparametric method that is suitable for detecting slowly-varying nonstationarities that can be seen as trends in the time marginal of the time-varying spectrum of the signal. The rationale behind the proposed method is to measure the importance of the trend in the time marginal by using a proper Test statistic, and further compare this measurement with the ones that are likely to be found in stationary references. It is shown that the distribution of the Test statistic under stationarity can be modeled fairly well by a Generalized Extreme Value (GEV) pdf, from which a threshold can be derived for Testing stationarity by means of a hypothesis Test. Finally, the new method is compared with other ones found in the literature.
-
A new Nonparametric method for Testing stationarity based on trend analysis in the time marginal distribution
2014Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:In this manuscript, we propose a novel Nonparametric Test for nonstationarities that are seen as a trend or an evolution in the local energy of the signal. The idea of the proposed technique consists in applying empirical mode decomposition for estimating and further quantifying the trend in the time marginal of the estimated time-frequency representation. Such methodology allows for the detection of slowly-varying nonstationarities of first and second-order.
Anne-catherine Favre - One of the best experts on this subject based on the ideXlab platform.
-
A Nonparametric Test for slowly-varying nonstationarities
Signal Processing, 2018Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity, atmospheric effects) and instrumental configurations (e.g., sensor noise), as well as material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.
-
a Nonparametric Test for slowly varying nonstationarities
Signal Processing, 2018Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:Abstract This paper develops a new Nonparametric method that is suitable for detecting slowly-varying nonstationarities that can be seen as trends in the time marginal of the time-varying spectrum of the signal. The rationale behind the proposed method is to measure the importance of the trend in the time marginal by using a proper Test statistic, and further compare this measurement with the ones that are likely to be found in stationary references. It is shown that the distribution of the Test statistic under stationarity can be modeled fairly well by a Generalized Extreme Value (GEV) pdf, from which a threshold can be derived for Testing stationarity by means of a hypothesis Test. Finally, the new method is compared with other ones found in the literature.
-
A new Nonparametric method for Testing stationarity based on trend analysis in the time marginal distribution
2014Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:In this manuscript, we propose a novel Nonparametric Test for nonstationarities that are seen as a trend or an evolution in the local energy of the signal. The idea of the proposed technique consists in applying empirical mode decomposition for estimating and further quantifying the trend in the time marginal of the estimated time-frequency representation. Such methodology allows for the detection of slowly-varying nonstationarities of first and second-order.
Douglas Baptista De Souza - One of the best experts on this subject based on the ideXlab platform.
-
A Nonparametric Test for slowly-varying nonstationarities
Signal Processing, 2018Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity, atmospheric effects) and instrumental configurations (e.g., sensor noise), as well as material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.
-
A new Nonparametric method for Testing stationarity based on trend analysis in the time marginal distribution
2014Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:In this manuscript, we propose a novel Nonparametric Test for nonstationarities that are seen as a trend or an evolution in the local energy of the signal. The idea of the proposed technique consists in applying empirical mode decomposition for estimating and further quantifying the trend in the time marginal of the estimated time-frequency representation. Such methodology allows for the detection of slowly-varying nonstationarities of first and second-order.
Douglas Baptista De Souza - One of the best experts on this subject based on the ideXlab platform.
-
a Nonparametric Test for slowly varying nonstationarities
Signal Processing, 2018Co-Authors: Douglas Baptista De Souza, Jocelyn Chanussot, Anne-catherine Favre, Pierre BorgnatAbstract:Abstract This paper develops a new Nonparametric method that is suitable for detecting slowly-varying nonstationarities that can be seen as trends in the time marginal of the time-varying spectrum of the signal. The rationale behind the proposed method is to measure the importance of the trend in the time marginal by using a proper Test statistic, and further compare this measurement with the ones that are likely to be found in stationary references. It is shown that the distribution of the Test statistic under stationarity can be modeled fairly well by a Generalized Extreme Value (GEV) pdf, from which a threshold can be derived for Testing stationarity by means of a hypothesis Test. Finally, the new method is compared with other ones found in the literature.
