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'.

翻译：热带森林因其在全球生态系统中的关键作用，其保护是一个具有重要社会与生态意义的课题。然而，森林砍伐和退化每年影响数百万公顷土地，因此需要政府或私营部门采取有效的森林监测举措。本研究提出了一种新框架，采用单变量边缘分布算法（UMDA）从Landsat-8卫星中选择光谱波段，以优化对砍伐区域的表征。该选择过程指导了语义分割架构DeepLabv3+，提升了其性能。实验结果表明，与常用于森林砍伐检测的波段组合相比，通过支持向量机（SVM）进行的片段分类发现，若干波段组合取得了更高的平衡准确率。此外，基于UMDA方法识别出的最优波段组合进一步提升了DeepLabv3+架构的性能，超过了本研究中对比的现有最优方法。少量选定波段优于全部波段的这一观察结果，与深度学习领域普遍盛行的数据驱动范式相矛盾。因此，这表明了“越多总是越好”的传统观念存在例外情况。