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.
The increase of the need f o r image storage and transmission in computer systems has increased the importance of signal and image compression algorithms. The approach involving vector quantization (VQ) relies on designing of a j k i t e set of codes which will substitute the original signal during transmission with a minimal of distortion, , taking advantage of the spatial redundancy of image to compress them. Algorithms such as LGB and SOM work in an unsupervised way toward finding a good codebook for a given training data. However, the number of code vectors ( N ) needed f o r VQ increases with the vector dimension, and full-search algorithms such as LGB and SOM can lead to large training and coding times. An alternative for reducing the computational complexity is the use of a tree-structured vector quantization algorithm. This paper presents an application of a hierarchical SOM f o r image compression in which reduces rhe search complexity from O(N) to O(1og N), enabling a faster training and image coding. Results are given for conventional SOM, LBG and HSOM, showing the advantage of the proposed method,
Clustering methods have been studied and applied in a diversity of problems involving multidimensional data. The objective is to classify N unlabeled objects in a P -dimensional space into groups based on their similarities. Difficulties include determining the real number of categories and a metric which optimally adapt to data. Conventional methods, such as k-means, may impose a structure on data rather than finding it. This paper focuses the usage of self-organizing feature map (SOM) as a clustering tool. Although SOM had been applied as visualization tool of high-dimensional data some additional procedures are required to enable a meaningful cluster's interpretation. It is shown that the map can be partitioned by analyzing inconsistent neighboring relations between neurons. The results are sets of connected neurons that represent data clusters. The number of clusters and its membership neurons are determined by the algorithm.
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