Single- and Multi-Objective Genetic Optimization of Hyperspectral Image Pre-Processing

Hyperspectral imaging has broad applications, particularly in Earth observation and remote sensing, due to its ability to provide large-scale, non-invasive environmental and agricultural monitoring. However, its high dimensionality and acquisition conditions pose challenges for data transfer, storage, and downstream analysis. Preprocessing algorithms are thus essential for preparing hyperspectral images for further analysis, but they rely on multiple hyperparameters, and their incorrect selection degrades the performance of machine learning models operating on the pre-processed imagery. To address this, we introduce genetic algorithms—both single- and multi-objective—for optimizing pre-processing in multi-target hyperspectral regression. Experiments on real-world hyperspectral data demonstrate that our approach significantly enhances the models for chlorophyll content estimation from hyperspectral images.