# Evolving multispectral sensor configurations using genetic programming for estuary health monitoring

## TL;DR

This paper introduces a genetic programming method to select key wavelengths in hyperspectral images for monitoring estuary sediment health.

## Contribution

A novel genetic programming approach for wavelength selection and feature extraction in hyperspectral image analysis is proposed.

## Key findings

- Spectra-based genetic programming solutions performed competitively compared to existing wavelength selection methods.
- SVR models using extracted features reliably predicted sediment organic matter content.
- The best-evolved solution was successfully applied to predict organic matter content across all collected images.

## Abstract

Assessing ecosystem health on a large scale is crucial for a wide range of management and regulatory decisions. Technologies such as hyperspectral imaging allow noninvasive and rapid estimation of key attributes based on observed reflectance. However, these images are high-dimensional and real-world applications require models based on fewer wavelengths. This paper proposes a new wavelength selection and feature extraction method for hyperspectral image analysis based on genetic programming to automatically select key wavelength regions and informative image features. A dataset of hyperspectral images of sediment in the field was collected and paired with ground-truth measurements of the sediment porosity and organic matter content. Two new program structures were proposed to construct feature extraction trees from either the mean reflectance spectra (spectra-based) or full hyperspectral images (image-based). SVR models were constructed to predict attributes based on the extracted features. Various regression models were used to predict the porosity and organic matter content. Full-wavelength models were constructed to reliably predict the organic matter content. The proposed spectra-based genetic programming solutions show competitive results compared to common wavelength selection methods, such as SPA, CARS, and RC. Finally, the best-evolved solution was applied to predict sediment organic matter content across all collected images.