Autoencoder-based Hyperspectral Anomaly Detection using Kernel Principal Component Pre-Processing.
Katinka MüllerVinay Chakravarthi GogineniMilica OrlandicStefan WernerPublished in: EUSIPCO (2023)
Keyphrases
- anomaly detection
- principal components
- hyperspectral
- kernel space
- preprocessing
- hyperspectral data
- principal component regression
- hyperspectral images
- remote sensing
- principal component analysis
- intrusion detection
- multispectral
- infrared
- spectral data
- detecting anomalies
- image data
- anomalous behavior
- hyperspectral imagery
- dimensionality reduction
- hyperplane
- feature extraction
- one class support vector machines
- principle component analysis
- intrusion detection system
- detect anomalies
- information content
- kernel function
- unsupervised learning
- feature set
- feature space
- partial least squares
- covariance matrix
- support vector
- kernel methods
- high dimensional data
- image processing