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Classification of Multi-spectral, Multi-temporal And Multi-sensor Images Using Principal Components Analysis and Artificial Neural Networks: Beykoz Case
Land Cover Classification Artificial Neural Networks
2015/7/9
The thematic maps derived from remotely-sensed images are invaluable sources of information for various investigations since they
provide spatial and temporal information about the nature of Earth su...
Entropy based determination of optimal principal components of Airborne Prism Experiment (APEX) imaging spectrometer data for improved land cover classification
High resolution data hyper spectral land cover classification feature extraction
2014/12/15
Hyperspectral data finds applications in the domain of remote sensing. However, with the increase in amounts of information and advantages associated, come the "curse" of dimensionality and additional...
Interpretation of Water Quality Data by Principal Components Analysis
Multivariate analysis principal components analysis quality variables river-basin
2009/10/14
A variety of methods are being used to display the information which is concealed in the quality variables observed in a water quality monitoring network. A large portion of these approaches are stati...
Improved performance of fault detection based on selection of the optimal number of principal components
Fault detection Fault SNR Sensitivity The number of PCs
2009/9/9
This paper presents a new method that selecting the number of principal components (PCs) in fault detection based on principal component analysis (PCA). Fault signal-to-noise ratio (SNR) is proposed a...