On the Use of the Principal Component Analysis (PCA) for Evaluating Vegetation Anomalies from LANDSAT-TM NDVI Temporal Series in the Basilicata Region (Italy).
Antonio LanorteTeresa ManziGabriele NolèRosa LasaponaraPublished in: ICCSA (4) (2015)
Keyphrases
- principal component analysis
- landsat tm
- remote sensing
- land cover
- satellite images
- remote sensing images
- change detection
- multispectral
- principal components
- dimensionality reduction
- covariance matrix
- face recognition
- independent component analysis
- remote sensing data
- soil erosion
- remotely sensed data
- high resolution
- anomaly detection
- dimension reduction
- supervised classification
- linear discriminant analysis
- feature space
- low dimensional
- geographic information systems
- image analysis
- remotely sensed
- discriminant analysis
- feature extraction
- dimension reduction methods
- hyperspectral
- image processing
- urban areas
- high quality
- face databases
- lower dimensional
- data streams
- singular value decomposition
- high dimensional