Abstract
The spectral content of the small number of training data may not be enough for the classification of high-dimensional hyperspectral images. For this reason, spatial information is also exploited next to the spectral information. In this study, it is intended to classify hyperspectral images using spatial features extracted by fuzzy C-means (FCM) and Dirichlet Mixture Model (DMM). The contribution of the cascaded use of proposed methods are presented in the results section by tables.
Keywords
Hyperspectral imaging
Pattern recognition (psychology)
Artificial intelligence
Spatial analysis
Fuzzy logic
Computer science
Dirichlet distribution
Latent Dirichlet allocation
Contextual image classification
Full spectral imaging
Image (mathematics)
Section (typography)
Computer vision
Remote sensing
Mathematics
Geography
Topic model
Subject Areas
Remote-Sensing Image Classification
·Media Technology ·Physical Sciences
Remote Sensing and Land Use
·Atmospheric Science ·Physical Sciences
Citations by Year
2018: 1
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