Sparse modeling for hyperspectral imagery with LiDAR data fusion for subpixel mapping

Castrodad, Alexey; Khuon, Timothy S.; Rand, Robert S.; Sapiro, Guillermo

doi:10.1109/igarss.2012.6351982

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…Alternatively, other fusion techniques, beyond neural net sensor fusion, such as the belief/mass Dempster Shafer and the linear-quadratic estimation Kalman filtering could be explored for sensor fusion. Further, the fully unsupervised sparse modeling approach for direct data fusion of HSI and LIDAR 8 is an alternative classification algorithm that could be combined with the neural net fusion algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, each distribution f b ðx; Θ i Þ can be fully characterized by the parameters Θ i ¼ fm i ; K i g, where m i and K i are the mean and covariance for class i. Figure 3 shows an example of three unimodal Gaussian distributions in a 2-D space that could be modeled using (8).…”

Section: Stochastic Mixture Modelmentioning

confidence: 99%

“…In the first process, PCD is segmented based on the multidimensional meanshift segmentation algorithm, where the results are clustered based on the geometry of points in the point cloud. In the second process, any of a number of unsupervised spectralonly 1,[7][8][9][10][11][12] or spectral-spatial methods [16][17][18][19][20][21][22][23][24] could be applied to HSI data to segment the data into a classification map of material compositions based on spectral information. We select the SEM as our choice algorithm to spectrally segment the data, because it is theoretically rigorous and more sophisticated than older segmentation methods, such as k-means 13 or ISODATA, 13 and because it can also especially be applied to MSI.…”

Section: Description Of Algorithmsmentioning

confidence: 99%

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Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion

2016

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Abstract. A proposed framework using spectral and spatial information is introduced for neural net multisensor data fusion. This consists of a set of independent-sensor neural nets, one for each sensor (type of data), coupled to a fusion net. The neural net of each sensor is trained from a representative data set of the particular sensor to map to a hypothesis space output. The decision outputs from the sensor nets are used to train the fusion net to an overall decision. During the initial processing, three-dimensional (3-D) point cloud data (PCD) are segmented using a multidimensional mean-shift algorithm into clustered objects. Concurrently, multiband spectral imagery data (multispectral or hyperspectral) are spectrally segmented by the stochastic expectation-maximization into a cluster map containing (spectral-based) pixel classes. For the proposed sensor fusion, spatial detections and spectral detections complement each other. They are fused into final detections by a cascaded neural network, which consists of two levels of neural nets. The success of the approach in utilizing sensor synergism for an enhanced classification is demonstrated for the specific case of classifying hyperspectral imagery and PCD extracted from LIDAR, obtained from an airborne data collection over the campus

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Section: Discussionmentioning

confidence: 99%

Section: Stochastic Mixture Modelmentioning

confidence: 99%

Section: Description Of Algorithmsmentioning

confidence: 99%

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Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion

2016

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“…However, such data can be useful to design an appropriate guidance map, i.e., a weighting function to be incorporated into the spatial regularizations. In [28], the authors used LiDAR data to calculate the weights describing spatial correlations in a local neighborhood for constraining spatial regularization. This study showed that including the weights into the spatial regularization can improve abundance estimates for regions that are partially occluded by a shadow.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization

Uezato

Fauvel²,

Dobigeon

2018

IEEE Trans. Geosci. Remote Sensing

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Spectral unmixing methods incorporating spatial regularizations have demonstrated increasing interest. Although spatial regularizers which promote smoothness of the abundance maps have been widely used, they may overly smooth these maps and, in particular, may not preserve edges present in the hyperspectral image. Existing unmixing methods usually ignore these edge structures or use edge information derived from the hyperspectral image itself. However, this information may be affected by large amounts of noise or variations in illumination, leading to erroneous spatial information incorporated into the unmixing procedure. This paper proposes a simple, yet powerful, spectral unmixing framework which incorporates external data (i.e. LiDAR data). The LiDAR measurements can be easily exploited to adjust standard spatial regularizations applied to the unmixing process. The proposed framework is rigorously evaluated using two simulated datasets and a real hyperspectral image. It is compared with competing methods that rely on spatial information derived from a hyperspectral image. The resultsshow that the proposed framework can provide better abundance estimates and, more specifically, can significantly improve the abundance estimates for pixels affected by shadows.

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“…Very recently, a Bayesian algorithm called "normal endmember spectral unmixing" has been proposed to enhance parameter estimation performed in the NCM. 11 Research has also been performed on the use of sparse modeling for modeling linear mixtures in hyperspectral imagery, 12,13 a data-driven stochastic approach, 14 as well as some advanced methods of nonnegative matrix factorization (NMF), such as projectionbased NMF, 15 and an adaptive L 1∕2 sparsity-constrained NMF. 16 Intimate mixtures exhibit nonlinear spectral mixing behavior and are modeled as nonlinear combinations of spectra from multiple endmembers.…”

Section: Introductionmentioning

confidence: 99%

Modeling linear and intimate mixtures of materials in hyperspectral imagery with single-scattering albedo and kernel approaches

Rand

Resmini

Allen

2017

J. Appl. Remote Sens

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Sparse modeling for hyperspectral imagery with LiDAR data fusion for subpixel mapping

Cited by 7 publications

References 9 publications

Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion

Distributed adaptive framework for multispectral/hyperspectral imagery and three-dimensional point cloud fusion

Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization

Modeling linear and intimate mixtures of materials in hyperspectral imagery with single-scattering albedo and kernel approaches

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