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.
This work presents a clusters analysis method which automatically finds the number of clusters as well as the partitioning of data set in a remotely sensed image without any type of assistance of an image analyst. The data clustering is made using the self-organizing (or Kohonen) map (SOM) and the techniques proposed by Costa & Netto [5] for automatic partition of trained SOM networks and for generating a hierarchy of maps based on the detected data clusters. The proposed clustering method has been applied on a LANDSAT/TM image and its performance was compared with that of K-means algorithm, conventionally used for remotely sensed images.
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