2024/04/03 by Eduardo B. Neto, Fábio A. Faria, Neto, Eduardo +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Earth and Planetary Sciences · Environmental Science · #Animal Vocal Communication and Behavior #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Marine animal studies overview #Neural and Evolutionary Computing (cs.NE) #Remote Sensing and Land Use
paper · pdf · doi:10.48550/arxiv.2404.02659
openalex publication_date 2024/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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'.