High Dimensionality Image Data
Mostrando 1-7 de 7 artigos, teses e dissertações.
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1. CLASS-BASED AFFINITY PROPAGATION FOR HYPERSPECTRAL IMAGE DIMENSIONALITY REDUCTION AND IMPROVEMENT OF MAXIMUM LIKELIHOOD CLASSIFICATION ACCURACY
Resumo Este artigo investiga um método de classificação alternativo que integra o algoritmo de clusterização propagação de afinidade baseado nas classes (PAC) e o Classificador Máxima Verossimilhança (MAXVER) com a finalidade de superar as limitações do MAXVER na classificação de dados de alta dimensionalidade e, assim, melhorar a sua acurácia.
Bol. Ciênc. Geod.. Publicado em: 18/04/2019
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2. On the classification of classes with nearly equal spectral response in remote sensing hyperspectral image data
It is well known that high-dimensional image data allows for the separation of classes that are spectrally very similar, i.e., possess nearly equal first-order statistics, provided that their second-order statistics differ significantly. The aim of this study is to contribute to a better understanding, from a more geometrically oriented point of view, of the
Publicado em: 2011
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3. Classificação de dados imagens em alta dimensionalidade, empregando amostras semi-rotuladas e estimadores para as probabilidades a priori / Classification of high dimensionality image data, using semilabeled samples and estimation of the a priori probabilities
Em cenas naturais, ocorrem com certa freqüência classes espectralmente muito similares, isto é, os vetores média são muito próximos. Em situações como esta dados de baixa dimensionalidade (LandSat-TM, Spot) não permitem uma classificação acurada da cena. Por outro lado, sabe-se que dados em alta dimensionalidade tornam possível a separação dest
Publicado em: 2008
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4. Evaluation of optimal and suboptimal feature selection methods applied to image textures / Avaliação de métodos ótimos e subótimos de seleção de características de texturas em imagens
Texture features are eficient image descriptors and can be employed in a wide range of applications, such as classification and segmentation. However, when the number of features is considerably high, pattern recognition tasks may be compromised. Feature selection helps prevent this problem, as it can be used to reduce data dimensionality and reveal features
Publicado em: 2008
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5. Extração de atributos de forma e seleção de atributos usando algoritmos genéticos para a classificação de regiões / Shape feature extraction and feature selection using genetic algorithm for region classification
Discrimination power of earth surface targets has been continuously bettered with the increasing remote sensors technological advances. Urban systems applications are one of the areas that recently took benefit of these advances, particularly when using high resolution remote sensing data. In this scale, however, it is often necessary to take into account th
Publicado em: 2007
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6. Classificação de regiões usando atributos de forma e seleção de atributos / Classification of region using shape feature and feature selection
With the steady increase in the number of features available from remote sensing sources, there is a growing necessity to reduce the complexity of the classification task. When data dimensionality is very high, a search strategy should be used to select the subset of features that gives the minimum classification error, considering the limited size of traini
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 01/04/2005
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7. Classification of region using shape feature and feature selection / Classificação de regiões usando atributos de forma e seleção de atributos
With the steady increase in the number of features available from remote sensing sources, there is a growing necessity to reduce the complexity of the classification task. When data dimensionality is very high, a search strategy should be used to select the subset of features that gives the minimum classification error, considering the limited size of traini
Publicado em: 2005