Linear unmixing of hyperspectral images using a scaled gradient method

Theys, C.; Dobigeon, Nicolas; Tourneret, Jean-Yves; Lantéri, H.

doi:10.1109/ssp.2009.5278458

Cited by 18 publications

(21 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Fully Constraint-Scale Gradient Method (FC-SGM) has been proposed in [18] as an alternative of FCLS to solve the linear unmixing problem under the non-negativity and sumto-one constrains using the scaled gradient method The criterion to be optimized is derived from the Lagrangian formulation including the non-negativity constraint. Then this criterion is minimized thanks to an iterative scaled gradient method.…”

Section: Fc-sgmmentioning

confidence: 99%

Accuracy and performance of linear unmixing techniques for detecting minerals on OMEGA/Mars express

Schmidt

Bourguignon²,

Mouëlic

et al. 2011

2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

Self Cite

View full text Add to dashboard Cite

Detecting minerals on a huge hyperspectral dataset (> To) is a difficult task that we proposed to address using linear unmixing techniques. We test different algorithms with positivity constrains on a typical Martian hyperspectral image of the Syrtis Major volcanic complex. The usefulness of additional constrains, such as sparsity and sum-to-one constrains are discussed. We compare the results with a supervised detection technique based on band ratio.

show abstract

Section: Fc-sgmmentioning

confidence: 99%

Accuracy and performance of linear unmixing techniques for detecting minerals on OMEGA/Mars express

Schmidt

Bourguignon²,

Mouëlic

et al. 2011

2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, several hyperspectral imaging-based spectral unmixing algorithms have been gradually introduced into MODIS FSC mapping. Zhang et al (2015) produced MODIS FSC maps using a fully constrained least squares spectral mixture analysis method (FCLS) [30], fully constrained scaled gradient method (FCSGM) [31], a sparse regression method (SPARSE) [32] and a polynomial nonlinear method (POLY) [33,34] for the Tibetan Plateau. Although they found that all of these algorithms can improve the accuracy of snow-cover mapping compared with the MOD10A1 FSC, they also showed that complex terrain can greatly reduce the accuracy of these spectral unmixing algorithms [35].…”

Section: Introductionmentioning

confidence: 99%

Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau

et al. 2017

View full text Add to dashboard Cite

Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1-R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1-R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring-it is the ideal algorithm for retrieving FSC for the Tibetan Plateau.

show abstract

“…Some examples are described in Heinz & Chang 2001;Theys et al 2009). For instance, the New Concepts in Imaging: Optical and Statistical Models FCLS method presented in (Heinz & Chang 2001) estimates the abundances by minimizing a mean-square-error criterion subject to linear equality and inequality constraints.…”

Section: Introductionmentioning

confidence: 99%

Supervised Nonlinear Unmixing of Hyperspectral Images Using a Pre-image Methods

Nguyen

Chen

Richard

et al. 2013

EAS Publications Series

View full text Add to dashboard Cite

Abstract. Spectral unmixing is an important issue to analyze remotely sensed hyperspectral data. This involves the decomposition of each mixed pixel into its pure endmember spectra, and the estimation of the abundance value for each endmember. Although linear mixture models are often considered because of their simplicity, there are many situations in which they can be advantageously replaced by nonlinear mixture models. In this chapter, we derive a supervised kernel-based unmixing method that relies on a pre-image problem-solving technique. The kernel selection problem is also briefly considered. We show that partially-linear kernels can serve as an appropriate solution, and the nonlinear part of the kernel can be advantageously designed with manifold-learning-based techniques. Finally, we incorporate spatial information into our method in order to improve unmixing performance.

show abstract

Linear unmixing of hyperspectral images using a scaled gradient method

Cited by 18 publications

References 12 publications

Accuracy and performance of linear unmixing techniques for detecting minerals on OMEGA/Mars express

Accuracy and performance of linear unmixing techniques for detecting minerals on OMEGA/Mars express

Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau

Supervised Nonlinear Unmixing of Hyperspectral Images Using a Pre-image Methods

Contact Info

Product

Resources

About