Extreme‐learning‐machine‐based land cover classification

@article{Pal2008ExtremelearningmachinebasedLC,
  title={Extreme‐learning‐machine‐based land cover classification},
  author={Mahesh Pal},
  journal={International Journal of Remote Sensing},
  year={2008},
  volume={30},
  pages={3835 - 3841}
}
  • M. Pal
  • Published 11 February 2008
  • Computer Science
  • International Journal of Remote Sensing
The extreme learning machine (ELM), a single hidden layer neural network based supervised classifier is used for remote sensing classifications. In comparison to the backpropagation neural network, which requires the setting of several user‐defined parameters and may produce local minima, the ELM requires setting of one parameter, and produces a unique solution for a set of randomly assigned weights. Two datasets, one multispectral and another hyperspectral, were used for classification… 
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References

SHOWING 1-10 OF 31 REFERENCES
The use of backpropagating artificial neural networks in land cover classification
TLDR
Results show that the use of ANNs with the settings recommended in this study can produce higher classification accuracies than either alternative.
A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery
TLDR
The backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition.
Classification of multispectral remote sensing data using a back-propagation neural network
The suitability of a back-propagation neural network for classification of multispectral image data is explored. A methodology is developed for selection of both training parameters and data sets for
An evaluation of some factors affecting the accuracy of classification by an artificial neural network
TLDR
The effect of four factors on the accuracy with which agricultural crops may be classified from airborne thematic mapper (ATM) data was investigated and a log-linear modelling approach was used to evaluate the simultaneous effect of the factors on classification accuracy.
Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification
TLDR
This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set and finds the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure.
An investigation of the design and use of feed-forward artificial neural networks in the classification of remotely sensed images
TLDR
A feed-forward network structure that learns the characteristics of the training data through the backpropagation learning algorithm is employed to classify land cover features using multispectral, multitemporal, and multisensory image data, and the results are found to be promising in terms of ease of design and use of ANNs.
Land surface classification by neural networks
TLDR
It is shown that the introduction of lateral network connections allows an easy clustering of the resulting topological feature space and is suggested for further work with this new type of classification algorithm.
Artificial Neural Networks for Land-Cover Classification and Mapping
  • D. Civco
  • Geography, Computer Science
    Int. J. Geogr. Inf. Sci.
  • 1993
TLDR
The application of artificial neural networks to the problem of deriving land-cover information from Landsat satellite Thematic Mapper digital imagery is described.
An assessment of the effectiveness of decision tree methods for land cover classification
TLDR
The results indicate that the performance of the univariate DT is acceptably good in comparison with that of other classifiers, except with high-dimensional data, and the use of attribute selection methods does not appear to be justified in terms of accuracy increases.
The application of artificial neural networks to the analysis of remotely sensed data
TLDR
An overview of the main concepts underlying ANNs, including the main architectures and learning algorithms, are presented, and the main tasks that involve ANNs in remote sensing are described.
...
1
2
3
4
...