Spectral Band Selection Methodology for Future Sensor Development

Hyperspectral imaging (HSI) significantly impacts diverse Earth observation applications, including precision agriculture, land use change monitoring, climate research, and natural resources management. By measuring hundreds of wavelengths along the electromagnetic spectrum, hyperspectral sensors can discriminate surfaces accurately, identify objects in fine detail and detect changes precisely. Nevertheless, HSI has some drawbacks, such as the challenge of handling vast amounts of information, the redundancy of spectral data, and the high price often associated with it. Therefore, a new model of band reduction based on artificial intelligence classifiers with hyperparameter optimisation and feature selectors is proposed. To test the model, it is applied to three popular HSI datasets: Pavia University, Indian Pines, and Salinas Valley. The results are promising; between 75% and 90% accuracy can be achieved with only four wavelengths. The best performance is for Salinas Valley, and the poorest is for Indian pines, coinciding with the ranking of dataset complexity regarding the number of classes and the similarity of spectral signatures. The results obtained are competitive with the models present in the literature, improving the classification of Indian Pines. The findings of this study suggest that it is feasible to develop multispectral sensors that require reduced spectral information, making them cost-effective and tailored to classify specific scenarios.