The effect of features reduction on different texture classifiers

Samawi, Venus W.; Basheer, O.

doi:10.1109/iciea.2008.4582482

Cited by 4 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analysis of hyperspectral imagery usually implicates the reduction of data set dimensionality to decrease the complexity of the classifier and the computational time required with the aim of preserving most of the relevant information of the original data according to some optimal or suboptimal criteria [3], [4]. The preprocessing procedure exploited in this section divides the hyperspectral signatures into adjacent regions of the spectrum and approximates their values by piecewise constant functions.…”

Section: A Reduction Of Data Dimensionalitymentioning

confidence: 99%

Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest

Licciardi

Pacifici

Tuia

et al. 2009

IEEE Trans. Geosci. Remote Sensing

161

View full text Add to dashboard Cite

Abstract-The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion TechnicalCommittee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm but also to provide a collaborative effort: The decision fusion of the best individual algorithms was aiming at further improving the classification performances, and the best algorithms were ranked according to their relative contribution to the decision fusion. This paper presents the five awarded algorithms and the conclusions of the contest, stressing the importance of decision fusion, dimension reduction, and supervised classification methods, such as neural networks and support vector machines.

show abstract

Section: A Reduction Of Data Dimensionalitymentioning

confidence: 99%

Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest

Licciardi

Pacifici

Tuia

et al. 2009

IEEE Trans. Geosci. Remote Sensing

161

View full text Add to dashboard Cite

show abstract

“…Eigenvalue Grads Method (EGM) [2] is a popular method for estimation the number of sources impinging on an array of sensors. The Principal Component Analysis (PCA) neural network [3][4] is widely used in applications ranging from neuroscience to signal processing to extract relevant information from high dimensional data sets. The PCA neural network has been found very useful for extracting the most representative low dimensional subspace from a high dimensional vector space.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach Combining Genetic Algorithm and Neural Networks for Eigen Value Grads Method (EGM) In Wireless Mobile Communications

Hussein Miry

2011

ETJ

View full text Add to dashboard Cite

The objective of this paper is combining Genetic Algorithm and Principal Component Analysis (PCA) neural network for Eigenvalue Grads Method (EGM) to estimate the number of sources in wireless mobile communications. The Eigenvalue Grads Method (EGM) is a popular method for estimation the number of sources impinging on an array of sensors, which is a problem of great interest in wireless mobile communications. This paper proposed a new system to estimate the number of sources by applying the output of genetic algorithm and PCA neural network with Complex Generalized Hebbian algorithm (CGHA) to EGM technique. In the proposed model, the initial weight and learning rate values for CGHA neural network can be selected automatically by using Genetic algorithm. The result of computer simulation for proposed system showed good response by fast converge speed for neural network , efficiency and yield the correct number of the sources. The important feature of new system is that, the PCA of covariance matrix are calculated based on CGHA neural network instead of determining the covariance matrix because computation of covariance matrix is time consuming.

show abstract

Automatic Machine Learning Classification Algorithms for Stability Detection of Smart Grid

Yousif

Samawi

Al-Saidi

2022

2022 IEEE 5th International Conference on Big Data and Artificial Intelligence (BDAI)

View full text Add to dashboard Cite

The effect of features reduction on different texture classifiers

Cited by 4 publications

References 2 publications

Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest

Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest

An Efficient Approach Combining Genetic Algorithm and Neural Networks for Eigen Value Grads Method (EGM) In Wireless Mobile Communications

Automatic Machine Learning Classification Algorithms for Stability Detection of Smart Grid

Contact Info

Product

Resources

About