Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Self-organizing maps
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extending the Kohonen self-organizing map networks for clustering analysis
Computational Statistics & Data Analysis
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Asymptotic Level Density of the Elastic Net Self-Organizing Feature Map
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Unsupervised Learning of Neural Network Ensembles for Image Classification
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Assessing Self-Organization Using Order Metrics
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Segmentation of multispectral remote sensing images using active support vector machines
Pattern Recognition Letters
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeding up the Self-Organizing Feature Map Using Dynamic Subset Selection
Neural Processing Letters
Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
Faster and more robust point symmetry-based K-means algorithm
Pattern Recognition
A new ART neural networks for remote sensing image classification
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Modern computational techniques for environmental data; application to the global ozone layer
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Clustering and visualizing SOM results
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Computers and Electronics in Agriculture
Using SOM to clustering of web sessions extracted by techniques of web usage mining
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Classification of pharmaceutical solid excipients using self-organizing maps
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Unlike conventional unsupervised classification methods, such as K-means and ISODATA, which are based on partitional clustering techniques, the methodology proposed in this work attempts to take advantage of the properties of Kohonen's self-organizing map (SOM) together with agglomerative hierarchical clustering methods to perform the automatic classification of remotely sensed images. The key point of the proposed method is to execute the cluster analysis process by means of a set of SOM prototypes, instead of working directly with the original patterns of the image. This strategy significantly reduces the complexity of the data analysis, making it possible to use techniques that have not normally been considered viable in the processing of remotely sensed images, such as hierarchical clustering methods and cluster validation indices. Through the use of the SOM, the proposed method maps the original patterns of the image to a two-dimensional neural grid, attempting to preserve the probability distribution and topology of the input space. Afterwards, an agglomerative hierarchical clustering method with restricted connectivity is applied to the trained neural grid, generating a simplified dendrogram for the image data. Utilizing SOM statistic properties, the method employs modified versions of cluster validation indices to automatically determine the ideal number of clusters for the image. The experimental results show examples of the application of the proposed methodology and compare its performance to the K-means algorithm.