Optimizing Bands Selection for Multispectral Cameras Through Genetic Algorithms

This study explores an alternative method for reflectance measurement using camera filters for spectral estimation, overcoming the limitations of traditional spectrophotometers, such as high costs and physical constraints. It focuses on optimizing band selection for imaging systems through Genetic Algorithms (GA), aiming to minimize color differences between reconstructed spectral data and actual measurements. The research considers both plants and colored objects to assess band selection effectiveness across combinations ranging from 4 to 9 bands. The study, conducted in the visible spectrum, involves vegetation samples, soil, and various color objects (pens, toys, and cartoons). Results indicate that color difference errors decrease with an increasing number of filters, plateauing beyond seven filters for both plant and object samples. Furthermore, the study highlights the importance of selecting optimal spectral bands for multispectral camera development using GA. By identifying the most informative band combinations while reducing redundancy, multispectral camera performance is enhanced across domains such as agriculture, environmental monitoring, and land cover analysis.