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.

该论文提出了一种采用 UMDA 算法选择 Landsat-8 的光谱波段，并通过 DeepLabv3 + 进行语义分割的新框架，用于监测热带森林的破坏。实验结果表明，使用 UMDA 算法选择的最佳组合 (651) 在分割分类任务中获得超过 90％的平衡准确性，效率和有效性优于其他组合。