Abundance Estimation - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Abundance Estimation

The Experts below are selected from a list of 3258 Experts worldwide ranked by ideXlab platform

Trac D. Tran – One of the best experts on this subject based on the ideXlab platform.

  • Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation
    IEEE Transactions on Geoscience and Remote Sensing, 2014
    Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

    Abstract:

    Sparsity-based unmixing algorithms, exploiting the sparseness property of the Abundances, have recently been proposed with promising performances. However, these algorithms are developed for the linear mixture model (LMM), which cannot effectively handle the nonlinear effects. In this paper, we extend the current sparse regression methods for the LMM to bilinear mixture models (BMMs), where the BMMs introduce additional bilinear terms in the LMM in order to model second-order photon scattering effects. To solve the Abundance Estimation problem for the BMMs, we propose to perform a sparsity-based Abundance Estimation by using two dictionaries: a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible second-order endmember interaction components. Then, the Abundance values can be estimated from the sparse codes associated with the linear dictionary. Moreover, to exploit the spatial data structure where the adjacent pixels are usually homogeneous and are often mixtures of the same materials, we first employ the joint-sparsity (row-sparsity) model to enforce structured sparsity on the Abundance coefficients. However, the joint-sparsity model is often a strict assumption, which might cause some aliasing artifacts for the pixels that lie on the boundaries of different materials. To deal with this problem, the low-rank-representation model, which seeks the lowest rank representation of the data, is further introduced to better capture the spatial data structure. Our simulation results demonstrate that the proposed algorithms provide much enhanced performance compared with state-of-the-art algorithms.

  • Low rank representation for bilinear Abundance Estimation problem
    2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
    Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

    Abstract:

    In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.

  • WHISPERS – Low rank representation for bilinear Abundance Estimation problem
    2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
    Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

    Abstract:

    In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.

Qing Qu – One of the best experts on this subject based on the ideXlab platform.

  • Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation
    IEEE Transactions on Geoscience and Remote Sensing, 2014
    Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

    Abstract:

    Sparsity-based unmixing algorithms, exploiting the sparseness property of the Abundances, have recently been proposed with promising performances. However, these algorithms are developed for the linear mixture model (LMM), which cannot effectively handle the nonlinear effects. In this paper, we extend the current sparse regression methods for the LMM to bilinear mixture models (BMMs), where the BMMs introduce additional bilinear terms in the LMM in order to model second-order photon scattering effects. To solve the Abundance Estimation problem for the BMMs, we propose to perform a sparsity-based Abundance Estimation by using two dictionaries: a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible second-order endmember interaction components. Then, the Abundance values can be estimated from the sparse codes associated with the linear dictionary. Moreover, to exploit the spatial data structure where the adjacent pixels are usually homogeneous and are often mixtures of the same materials, we first employ the joint-sparsity (row-sparsity) model to enforce structured sparsity on the Abundance coefficients. However, the joint-sparsity model is often a strict assumption, which might cause some aliasing artifacts for the pixels that lie on the boundaries of different materials. To deal with this problem, the low-rank-representation model, which seeks the lowest rank representation of the data, is further introduced to better capture the spatial data structure. Our simulation results demonstrate that the proposed algorithms provide much enhanced performance compared with state-of-the-art algorithms.

  • Low rank representation for bilinear Abundance Estimation problem
    2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
    Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

    Abstract:

    In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.

  • WHISPERS – Low rank representation for bilinear Abundance Estimation problem
    2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
    Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

    Abstract:

    In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.

Nasser M. Nasrabadi – One of the best experts on this subject based on the ideXlab platform.

  • Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation
    IEEE Transactions on Geoscience and Remote Sensing, 2014
    Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

    Abstract:

    Sparsity-based unmixing algorithms, exploiting the sparseness property of the Abundances, have recently been proposed with promising performances. However, these algorithms are developed for the linear mixture model (LMM), which cannot effectively handle the nonlinear effects. In this paper, we extend the current sparse regression methods for the LMM to bilinear mixture models (BMMs), where the BMMs introduce additional bilinear terms in the LMM in order to model second-order photon scattering effects. To solve the Abundance Estimation problem for the BMMs, we propose to perform a sparsity-based Abundance Estimation by using two dictionaries: a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible second-order endmember interaction components. Then, the Abundance values can be estimated from the sparse codes associated with the linear dictionary. Moreover, to exploit the spatial data structure where the adjacent pixels are usually homogeneous and are often mixtures of the same materials, we first employ the joint-sparsity (row-sparsity) model to enforce structured sparsity on the Abundance coefficients. However, the joint-sparsity model is often a strict assumption, which might cause some aliasing artifacts for the pixels that lie on the boundaries of different materials. To deal with this problem, the low-rank-representation model, which seeks the lowest rank representation of the data, is further introduced to better capture the spatial data structure. Our simulation results demonstrate that the proposed algorithms provide much enhanced performance compared with state-of-the-art algorithms.

  • Low rank representation for bilinear Abundance Estimation problem
    2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
    Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

    Abstract:

    In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.

  • WHISPERS – Low rank representation for bilinear Abundance Estimation problem
    2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
    Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. Tran

    Abstract:

    In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.