# A Satellite Band Selection Framework for Amazon Forest Deforestation Detection Task

arXiv: https://arxiv.org/abs/2404.02659 · HTML: https://mlsanity.com/paper/2404.02659/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 45 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [ForestEyes Project: Conception, Enhancements, and Challenges](https://mlsanity.com/paper/2208.11687/) (2022)
- [Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests](https://mlsanity.com/paper/2208.11058/) (2022)

## Built on by (most cited first, 2 of 2)

- [How to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests](https://mlsanity.com/paper/2409.04330/) (2024)
- [Do Superpixel Segmentation Methods Influence Deforestation Image Classification?](https://mlsanity.com/paper/2510.04645/) (2025)

## Abstract (arXiv)

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'.