On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images

Plaza, Javier; Pérez, R.; Martı́nez, Pablo

doi:10.1016/j.patcog.2009.04.008

Cited by 91 publications

(48 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [23], the authors designed a multi-layer perceptron neural network combined with a Hopfield neural network to deal with nonlinear mixtures. In [6], [24], the authors discussed methods for automatic selection and labeling of training samples. These methods require the networks to be trained using pixels with known abundances, and the quality of the training data may affect the performance notably.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model

Chen

Richard

Honeiné

2013

IEEE Trans. Signal Process.

158

205

View full text Add to dashboard Cite

Abstract-Spectral unmixing is an important issue to analyze remotely sensed hyperspectral data. Although the linear mixture model has obvious practical advantages, there are many situations in which it may not be appropriate and could be advantageously replaced by a nonlinear one. In this paper, we formulate a new kernel-based paradigm that relies on the assumption that the mixing mechanism can be described by a linear mixture of endmember spectra, with additive nonlinear fluctuations defined in a reproducing kernel Hilbert space. This family of models has clear interpretation, and allows to take complex interactions of endmembers into account. Extensive experiment results, with both synthetic and real images, illustrate the generality and effectiveness of this scheme compared with state-of-the-art methods.Index Terms-Hyperspectral imaging, multi-kernel learning, nonlinear spectral unmixing, support vector regression.

show abstract

Section: Introductionmentioning

confidence: 99%

Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model

Chen

Richard

Honeiné

2013

IEEE Trans. Signal Process.

158

205

View full text Add to dashboard Cite

show abstract

“…Several techniques have been proposed for such purposes [4], but all of them are very expensive in computational terms. Although these techniques map nicely to high performance computing systems such as commodity clusters [9], these systems are difficult to adapt to on-board processing requirements introduced by applications, such as wildland fire tracking, biological threat detection, monitoring of oil spills, and other types of chemical contamination.…”

Section: Introductionmentioning

confidence: 99%

Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units

Sánchez¹,

Paz²,

Martín³

2011

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

SUMMARYHyperspectral imaging instruments are capable of collecting hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. One of the main problems in the analysis of hyperspectral data cubes is the presence of mixed pixels, which arise when the spatial resolution of the sensor is not enough to separate spectrally distinct materials. Hyperspectral unmixing is one of the most popular techniques to analyze hyperspectral data. It comprises two stages: (i) automatic identification of pure spectral signatures (endmembers) and (ii) estimation of the fractional abundance of each endmember in each pixel. The spectral unmixing process is quite expensive in computational terms, mainly due to the extremely high dimensionality of hyperspectral data cubes. Although this process maps nicely to high performance systems such as clusters of computers, these systems are generally expensive and difficult to adapt to real-time data processing requirements introduced by several applications, such as wildland fire tracking, biological threat detection, monitoring of oil spills, and other types of chemical contamination. In this paper, we develop an implementation of the full hyperspectral unmixing chain on commodity graphics processing units (GPUs). The proposed methodology has been implemented, using the CUDA (compute device unified architecture), and tested on three different GPU architectures: NVidia Tesla C1060, NVidia GeForce GTX 275, and NVidia GeForce 9800 GX2, achieving near real-time unmixing performance in some configurations tested when analyzing two different hyperspectral images, collected over the World Trade Center complex in New York City and the Cuprite mining district in Nevada.

show abstract

“…Therefore, the optimal required numbers of hidden layers and neurons are specified by NN experiments depending on trial and error as applied, for instance, in [28,29]. If the required performance is not reached in the test process, this system is continued to do another new experiment.…”

Section: Proposed Systemmentioning

confidence: 99%

Reorganizing Neural Network System for Two Spirals and Linear Low-Density Polyethylene Copolymer Problems

Behery

El-Harby

El-Bakry

2009

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

This paper presents an automatic system of neural networks (NNs) that has the ability to simulate and predict many of applied problems. The system architectures are automatically reorganized and the experimental process starts again, if the required performance is not reached. This processing is continued until the performance obtained. This system is first applied and tested on the two spiral problem; it shows that excellent generalization performance obtained by classifying all points of the two-spirals correctly. After that, it is applied and tested on the shear stress and the pressure drop problem across the short orifice die as a function of shear rate at different mean pressures for linear low-density polyethylene copolymer (LLDPE) at 190• C. The system shows a better agreement with an experimental data of the two cases: shear stress and pressure drop. The proposed system has been also designed to simulate other distributions not presented in the training set (predicted) and matched them effectively.

show abstract

On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images

Cited by 91 publications

References 32 publications

Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model

Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model

Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units

Reorganizing Neural Network System for Two Spirals and Linear Low-Density Polyethylene Copolymer Problems

Contact Info

Product

Resources

About