A Framework of Landsat-8 Band Selection based on UMDA for Deforestation
  Detection
Eduardo B. Neto Paulo R. C. Pedro Alvaro Fazenda Fabio A. Faria
The conservation of tropical forests is a current subject of social and
ecological relevance due to their crucial role in the global ecosystem.
Unfortunately, millions of hectares are deforested and degraded each year.
Therefore, government or private initiatives are needed for monitoring tropical
forests. In this sense, this work proposes a novel framework, which uses of
distribution estimation algorithm (UMDA) to select spectral bands from
Landsat-8 that yield a better representation of deforestation areas to guide a
semantic segmentation architecture called DeepLabv3+. In performed experiments,
it was possible to find several compositions that reach balanced accuracy
superior to 90% in segment classification tasks. Furthermore, the best
composition (651) found by UMDA algorithm fed the DeepLabv3+ architecture and
surpassed in efficiency and effectiveness all compositions compared in this
work.