Machine Learning with Spectral Evolutions High Resolution Hyperspectral Data

Spectral Evolution has continued to integrate emerging analytical methods into its workflows, in order to leverage its ultra-high resolution spectral data. Machine learning techniques have rapidly become a central frontier in remote sensing research.  These approaches are now widely used across diverse applications, including climate science, water quality assessment, mineral exploration, and precision agriculture. A defining characteristic of remote sensing in these applications is the use of large, high-dimensional datasets. Machine learning methods are uniquely suited to extract value from these complex datasets by identifying patterns, isolating contributing spectral components, and enabling quantitative analysis of underlying properties.

Most machine learning methods start with data preprocessing. One of the most common approaches is the calculation of derivative spectra. Derivative spectra

are effective at minimizing the influence of illumination variability caused by factors such as solar angle, cloud cover, and topographical effects.

Derivatives can be sensitive to noise which is why it is common to apply smoothing techniques. A widely used smoothing method is the Savitzky–Golay filter, which employs a localized least-squares polynomial fit to preserve spectral features while reducing random noise. This approach assumes that noise characteristics are relatively consistent across the spectrum, allowing for effective signal enhancement without significant distortion of absorption features.

Once preprocessed, hyperspectral data can be analyzed using a variety of machine learning techniques. Two of the most commonly applied methods are Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). PCA is an unsupervised dimensionality reduction technique that decomposes the dataset into orthogonal components representing the dominant sources of variance. This enables spectral unmixing and identification of key spectral patterns without prior knowledge of the system.

In contrast, PLSR is a supervised method that establishes relationships between spectral data and measured reference variables. This is where laborator

y measurements can be used to train a prediction model. Once trained, these models can estimate the quantity of metrics from spectral reflectance, enabling rapid and non-destructive analysis.

Common remote sensing techniques often rely on coarse spectral resolution, limiting their ability to capture subtle yet meaningful spectral features. In contrast, high-resolution hyperspectral methods enhance the capacity to spectrally unmix optically complex samples, enable more accurate predictive models for quantitative analysis. Spectral Evolution, the leading manufacturer of high-resolution hyperspectral spectroradiometers, has integrated the Eigenvector Solo model products into DARWin SP, allowing users to analyze spectral data with machine learning models in real time during field collection.

As spectral libraries and datasets continue to grow in both size and resolution, new opportunities for advanced data analysis emerge. High-quality spectral data, combined with appropriate preprocessing and robust machine learning techniques, is critical to fully realizing the potential of high resolution hyperspectral remote sensing.