A Single Classifier Using Principal Components Vs Multi-Classifier System: In Landuse-LandCover Classification of WorldView-2 Sensor Data

In remote sensing community, Principal Component Analysis (PCA) is widely utilized for dimensionality reduction in order to deal with high spectral-dimension data. However, dimensionality reduction through PCA results in loss of some spectral information. Analysis of an Earth-scene, based on first few principal component bands/channels, introduces error in classification, particularly since the dimensionality reduction in PCA does not consider accuracy of classification as a requirement. The present research work explores a different approach called Multi-Classifier System (MCS)/Ensemble classification to analyse high spectral-dimension satellite remote sensing data of WorldView-2 sensor. It examines the utility of MCS in landuse-landcover (LULC) classification without compromising any channel i.e. avoiding loss of information by utilizing all of the available spectral channels. It also presents a comparative study of classification results obtained by using only principal components by a single classifier and using all the original spectral channels in MCS. Comparative study of the classification results in the present work, demonstrates that utilizing all channels in MCS of five Artificial Neural Network classifiers outperforms a single Artificial Neural Network classifier that uses only first three principal components for classification process.

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Introducing Asymmetry into Interneuron Learning

Neural Computation ◽

10.1162/neco.1995.7.6.1191 ◽

1995 ◽

Vol 7 (6) ◽

pp. 1191-1205 ◽

Cited By ~ 16

Author(s):

Colin Fyfe

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Negative Feedback ◽

Principal Components ◽

Network Architecture ◽

Hebbian Learning ◽

Principal Component ◽

Neural Network Architecture ◽

Weight Decay ◽

Artificial Neural Network Architecture

A review is given of a new artificial neural network architecture in which the weights converge to the principal component subspace. The weights learn by only simple Hebbian learning yet require no clipping, normalization or weight decay. The net self-organizes using negative feedback of activation from a set of "interneurons" to the input neurons. By allowing this negative feedback from the interneurons to act on other interneurons we can introduce the necessary asymmetry to cause convergence to the actual principal components. Simulations and analysis confirm such convergence.

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Adaptive dimensionality reduction for neural network-based online principal component analysis

PLoS ONE ◽

10.1371/journal.pone.0248896 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0248896

Author(s):

Nico Migenda ◽

Ralf Möller ◽

Wolfram Schenck

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Dimensionality Reduction ◽

Principal Components ◽

Principal Component ◽

Component Analysis ◽

Optimal Number ◽

Streaming Data ◽

Number Of Principal Components ◽

Incremental Pca

“Principal Component Analysis” (PCA) is an established linear technique for dimensionality reduction. It performs an orthonormal transformation to replace possibly correlated variables with a smaller set of linearly independent variables, the so-called principal components, which capture a large portion of the data variance. The problem of finding the optimal number of principal components has been widely studied for offline PCA. However, when working with streaming data, the optimal number changes continuously. This requires to update both the principal components and the dimensionality in every timestep. While the continuous update of the principal components is widely studied, the available algorithms for dimensionality adjustment are limited to an increment of one in neural network-based and incremental PCA. Therefore, existing approaches cannot account for abrupt changes in the presented data. The contribution of this work is to enable in neural network-based PCA the continuous dimensionality adjustment by an arbitrary number without the necessity to learn all principal components. A novel algorithm is presented that utilizes several PCA characteristics to adaptivly update the optimal number of principal components for neural network-based PCA. A precise estimation of the required dimensionality reduces the computational effort while ensuring that the desired amount of variance is kept. The computational complexity of the proposed algorithm is investigated and it is benchmarked in an experimental study against other neural network-based and incremental PCA approaches where it produces highly competitive results.

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Prediction of Peak Velocity of Blasting Vibration Based on Artificial Neural Network Optimized by Dimensionality Reduction of FA-MIV

Mathematical Problems in Engineering ◽

10.1155/2018/8473547 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 10

Author(s):

Zhang Zhongya ◽

Jin Xiaoguang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Analysis ◽

Dimensionality Reduction ◽

Particle Velocity ◽

Principal Components ◽

Common Factors ◽

Output Parameter ◽

Blasting Vibration ◽

Ann Models

Blasting vibration is harmful to the nearby habitants and dwellings in diverse geotechnical engineering. In this paper, a novel scheme based on Artificial Neural Network (ANN) method optimized by dimensionality reduction of Factor Analysis and Mean Impact Value (FA-MIV) is proposed to predict peak particle velocity (PPV) of blasting vibration. To construct the model, nine parameters of field measurement are taken as undetermined input parameters for research, while peak particle velocity (PPV) is considered as output parameter. With the application of FA, common factors are extracted from undetermined input parameters. Then, principal components are defined as a linear combination of common factors. The weight of each principal components effected on output parameter is ranked according to the calculation of MIV, and two principal components with minimum weight are eliminated. Ultimately, output parameter (PPV) is explained in a low-dimensional space with four input characteristic parameters. In the prepared database consisting of 108 datasets, 98 datasets are used for the training of the model, while the rest are used for testing performance. The performances of the ANN models are compared with regression analysis, in terms of coefficient of determination (R2) and mean absolute error (MAE). It is found that the performances of ANN models with using FA-MIV are superior to those of models without using FA-MIV in the prediction of PPV. In addition, the abilities of ANN models are all superior to regression analysis in the prediction of PPV. The result obtained from ELM is more accurate than BPNN and MVRA models.

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Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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