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.

翻译：热带雨林的保护因在全球生态系统中扮演关键角色而成为当前具有社会与生态意义的话题。不幸的是，每年有数百万公顷的森林被砍伐和退化。因此，需要政府或私人机构的倡议来监测热带森林。在此背景下，本文提出一种新颖框架，该框架采用分布估计算法（UMDA）从Landsat-8中选择光谱波段，以更好地表征森林砍伐区域，从而引导名为DeepLabv3+的语义分割架构。在实验中发现，多种波段组合在分割分类任务中均能达到超过90%的平衡准确率。此外，由UMDA算法找到的最优波段组合（651）输入DeepLabv3+架构后，在效率和有效性上均超越了本文所对比的所有组合。