3.2. Dimensionality Reduction

Hyperspectral datasets can contain hundreds of spectral bands, many of which may be redundant or noisy. Dimensionality reduction techniques are essential to simplify data, reduce computational load, and enhance meaningful features.

Principal Component Analysis (PCA)

• Transforms correlated bands into a smaller set of uncorrelated components.
• Captures the majority of data variance in the first few components.
• Helps visualise and separate key features in data (e.g., vegetation vs. soil).

Minimum Noise Fraction (MNF)

• An enhanced PCA that first estimates and minimises noise contributions before dimensionality reduction.
• MNF transforms maximise the signal-to-noise ratio (SNR), making it more suitable for hyperspectral data.

t-Distributed Stochastic Neighbour Embedding (t-SNE)

• A non-linear dimensionality reduction method.
• Effective for visualising high-dimensional data in 2D or 3D scatter plots.
• Preserves local relationships between data points, useful for pattern discovery and cluster analysis.