3.3. Classification and Target Detection

Classification in hyperspectral imaging refers to the process of assigning each pixel in an image to a specific class or material type based on its spectral properties. This is a core analytical step that transforms raw spectral data into meaningful thematic maps—such as vegetation species maps, mineral deposit maps, or land cover classifications.

Hyperspectral classification techniques broadly fall into two categories:

Supervised Classification

Supervised classification requires prior knowledge of the materials or classes present in the scene. It involves training the classifier using labelled datasets, where specific regions of the image (or spectral signatures) are pre-identified as belonging to a particular class.

Key Characteristics:

• Training Data Required: Needs accurate ground-truth or spectral library references.
• Higher Accuracy: Generally provides more precise classification results when good training data is available.
• Customisable: Can be fine-tuned to detect specific materials or conditions (e.g., a particular crop disease).

Common Supervised Methods:

• Spectral Angle Mapper (SAM): A physically intuitive algorithm that measures the angle between the pixel’s spectrum and a reference spectrum. Smaller angles indicate a closer match.
  • Strengths: Robust against illumination differences.
  • Limitations: Less effective when materials have subtle spectral differences.
• Support Vector Machines (SVM): A machine learning classifier that finds the optimal boundary (hyperplane) separating classes in a high-dimensional space.
  • Strengths: Effective in high-dimensional hyperspectral data; handles non-linear separability.
  • Limitations: Computationally intensive with large datasets.
• Random Forest (RF): An ensemble learning method using multiple decision trees. Each tree contributes to a “vote” for the final classification.
  • Strengths: High accuracy; less prone to overfitting; handles noise well.
  • Limitations: Requires careful tuning of hyperparameters like the number of trees.

When to Use Supervised Classification:

• You have reliable ground-truth or labelled training data.
• You need precise material-level classification (e.g., crop species mapping, mineral detection).
• The scene involves spectrally similar materials requiring finely tuned models.

Unsupervised Classification

Unsupervised classification, on the other hand, does not require prior knowledge of the classes in the scene. Instead, it relies on statistical algorithms to group pixels based on similarities in their spectral signatures, forming clusters that ideally correspond to real-world classes.

Key Characteristics:

• No Training Data Needed: Automatically groups pixels into clusters based on spectral similarity.
• Exploratory Tool: Useful for initial scene exploration and unknown environments.
• Subject to Interpretation: Clusters may not directly correspond to meaningful classes without further analysis.

Common Unsupervised Methods:

• K-Means Clustering: Partitions pixels into ‘k’ clusters by minimising within-cluster variance.
  • Strengths: Simple and fast.
  • Limitations: Requires defining the number of clusters (k) in advance.
• ISODATA (Iterative Self-Organizing Data Analysis Technique): An advanced clustering method that can automatically merge and split clusters based on variance and cluster sizes.
  • Strengths: More flexible than K-Means.
  • Limitations: Computationally heavier.

When to Use Unsupervised Classification:

• You lack sufficient ground-truth data.
• You’re performing exploratory data analysis in unfamiliar areas.
• You need to detect general land cover patterns or anomalies.

Supervised vs. Unsupervised: Choosing the Right Approach

Target Detection in Hyperspectral Imaging

Target detection is a specialised classification task focused on identifying specific, often rare, materials within a scene, even when they occupy only a small fraction of a pixel (sub-pixel detection).

Key techniques include:

• Matched Filtering (MF): Maximises detection of a known target signature while suppressing background.
• Adaptive Cosine Estimator (ACE): Measures similarity between a pixel’s spectrum and the target signature, normalising for background variability.
• RX Anomaly Detector: Identifies pixels that significantly deviate from the scene’s average spectral behaviour, useful when the target is unknown.

Key Considerations for Effective Classification & Target Detection

• Quality and representativeness of training data (for supervised methods).
• Dimensionality reduction to mitigate the “curse of dimensionality.”
• Noise removal and preprocessing to enhance signal clarity.
• Algorithm selection based on the scene complexity and computational resources.
• Post-classification validation using ground truth or independent datasets.

Summary

• Each material has a unique spectral fingerprint.
• Spectral libraries (field, lab, or satellite-based) enable material identification and comparison.
• Hyperspectral data is complex and has many redundant/noisy bands.
• Dimensionality reduction methods, like PCA, MNF, and t-SNE compress data while preserving key spectral information.
• Classification & Target Detection
  • Supervised: Uses labelled data for precise mapping (e.g., SVM, Random Forest, SAM).
  • Unsupervised: Groups pixels automatically for exploration (e.g., K-Means, ISODATA).
  • Target detection methods (MF, ACE, RX) identify rare or subtle materials, even sub-pixel.

Spectral analysis transforms raw hyperspectral cubes into actionable insights by reducing complexity, enhancing interpretability, and enabling precise detection of materials and anomalies.