Methodology for Band Selection of Hyperspectral Images Using Genetic Algorithms and Gaussian Maximum Likelihood Classifier

Remote Sensing is a technique to obtain data from objects without physical contact using sensors. If it is performed by hyperspectral sensors, then is possible to have a large number of bands that allows many applications. In this case, a large amount of data needs to be processed and analyzed, and therefore the practical use of these images have challenges with storage, transport and processing time. Another issue is that many of these bands contain redundant information due to high correlation between them that can cause instability in the convergence of computing processes. Because of these problems there is a need to select important bands to be processed. In many monitoring applications through remote sensing prior knowledge of areas can be used to improve the processing. Algorithms that use this information are known as supervised methods. This study introduces a methodology for band selection based on Genetic Algorithms and Gaussian Maximum Likelihood Classifier. The results were significant and demonstrate that the proposed method can reduce about 26,56% of the number of bands in hyperspectral images maintaining good quality for interpretation. The result of the similarity of the segmentation of images generated with fewer bands and more bands in the proposed methodology and the other algorithm were also compared. With the results expected to apply the proposed methodology in practical work and further study.