Abstract
Vegetation indices are algebraic combinations of spectral bands produced by satellite. The indices allow different vegetation covers to be identified by contrast evaluation. Vegetation indexes are used mainly in tasks of classification of satellite images, as well as chemical and physical land studies. An example is seen in the Normalized Difference Vegetation Index (NDVI) that shows up live green vegetation. This article describes the process of creating a new vegetation index that enables bare ground identification in the Amazon using genetic programming. It further shows how a threshold is automatically defined for the new index, a threshold that facilitates the task of photointerpretation and is not normally provided for other vegetation indexes. The new index, called BGIGP (Bare Ground Index obtained using Genetic Programming) showed significant values of contrast between the different covers analyzed, being seen to compete well with traditional vegetation indexes such as SR. The performance of BGIGP was also evaluated using the characteristics of 10448-pixel images from the “2017 Kaggle Planet: Understanding the Amazon from Space” competition, to classify bare ground against water, cloudy, primary, cultivation, road, and artisanal mine, obtaining a 93.71% of accuracy.
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Muñoz, J., Cobos, C., Mendoza, M. (2018). Vegetation Index Based on Genetic Programming for Bare Ground Detection in the Amazon. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Computational Intelligence. MICAI 2017. Lecture Notes in Computer Science(), vol 10633. Springer, Cham. https://doi.org/10.1007/978-3-030-02840-4_21
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