Unsupervised methods of classifying remotely sensed imges using Kohonen self-organizing maps / Metodos de classificação não-supervisionada de imagens de sensoriamento remoto usando mapas auto-organizaveis de Kohonen
AUTOR(ES)
Marcio Leandro Gonçalves
DATA DE PUBLICAÇÃO
2009
RESUMO
This thesis proposes new methods of unsupervised classification for remotely sensed images which particularly exploit the characteristics and properties of the Kohonen Self-Organizing Map (SOM). The key point is to execute the clustering process through a set of prototypes of SOM instead of analyzing directly the original patterns of the image. This strategy significantly reduces the complexity of data analysis, making it possible to use techniques that have not usually been considered computationally viable for processing remotely sensed images, such as hierarchical clustering methods and cluster validation indices. Unlike other approaches in which SOM is used as a visual tool for detection of clusters, the proposed classification methods automatically analyze the neurons grid of a trained SOM in order to find better partitions for data sets of images. Based on the statistical properties of the SOM, clustering validation indices calculated in a modified manner are proposed with the aim of reducing the computational cost of the classification process of images. Image texture analysis techniques are applied to evaluate and filter training samples and/or prototypes of the SOM that correspond to transition regions between land cover classes. Spatial information about the prototypes of the SOM, in addition to multiespectral distance information, are also incorporated in criteria for merging clusters with aim to facilitate the discrimination of land cover classes which have high spectral similarity. Experimental results show that the proposed classification methods present significant advantages when compared to unsupervised classification techniques frequently used in remote sensing.
ASSUNTO(S)
neural networks sensoriamento remoto kohonen maps processamento de imagens - técnicas digitais remote sensing inteligencia artificial artificial intelligence mapas auto-organizaveis redes neurais (computação) digital image processing
ACESSO AO ARTIGO
http://libdigi.unicamp.br/document/?code=000442801Documentos Relacionados
- Mapas auto-organizáveis na análise exploratória de dados geoespaciais multivariados
- UNSUPERVISED CLASSIFICATION OF SATELLITE IMAGES
- Categorização de imagens médicas baseada em transformada wavelet e mapas auto-organizáveis.
- Análise de Sinais Eletrocardiográficos Atriais Utilizando Componentes Principais e Mapas Auto-Organizáveis.
- Planejamento territorial: análise espacial de área com mapas auto-organizáveis de Kohonen.