  The conservation of tropical forests is a topic of significant social and
ecological relevance due to their crucial role in the global ecosystem.
Unfortunately, deforestation and degradation impact millions of hectares
annually, necessitating government or private initiatives for effective forest
monitoring. This study introduces a novel framework that employs the Univariate
Marginal Distribution Algorithm (UMDA) to select spectral bands from Landsat-8
satellite, optimizing the representation of deforested areas. This selection
guides a semantic segmentation architecture, DeepLabv3+, enhancing its
performance. Experimental results revealed several band compositions that
achieved superior balanced accuracy compared to commonly adopted combinations
for deforestation detection, utilizing segment classification via a Support
Vector Machine (SVM). Moreover, the optimal band compositions identified by the
UMDA-based approach improved the performance of the DeepLabv3+ architecture,
surpassing state-of-the-art approaches compared in this study. The observation
that a few selected bands outperform the total contradicts the data-driven
paradigm prevalent in the deep learning field. Therefore, this suggests an
exception to the conventional wisdom that 'more is always better'.
