Abstract
The preservation of the Amazon Rainforest is one of the global priorities in combating climate change, protecting biodiversity, and safeguarding indigenous cultures. The Satellite-based Monitoring Project of Deforestation in the Brazilian Legal Amazon (PRODES), a project of the National Institute for Space Research (INPE), stands out as a fundamental initiative in this effort, annually monitoring deforested areas not only in the Amazon but also in other Brazilian biomes. Recently, machine learning models have been developed using PRODES data to support this effort through the comparative analysis of multitemporal satellite images, treating deforestation detection as a change detection problem. However, existing approaches present significant limitations: models evaluated in the literature still show unsatisfactory effectiveness, many do not incorporate modern architectures, such as those based on self-attention mechanisms, and there is a lack of methodological standardization that allows direct comparisons between different studies. In this work, we address these gaps by evaluating various change detection models in a unified dataset, including fully convolutional models and networks incorporating self-attention mechanisms based on Transformers. We investigate the impact of different pre- and post-processing techniques, such as filtering deforested areas predicted by the models based on the size of connected components, texture replacement, and image enhancements; we demonstrate that such approaches can significantly improve individual model effectiveness. Additionally, we test different strategies for combining the evaluated models to achieve results superior to those obtained individually, reaching an F1-score of 80.41%, a value comparable to other recent works in the literature.
Abstract (translated)
亚马逊雨林的保护是应对气候变化、保护生物多样性以及守护土著文化的一项全球性优先任务。巴西法律亚马逊地区的毁林卫星监测项目（PRODES）是由国家空间研究所（INPE）发起的一个关键举措，该项目通过年度监测来跟踪整个亚马孙地区及其他巴西生物群落中的毁林情况。最近，研究人员利用PRODES数据开发了机器学习模型，以支持这项工作，通过对多时间序列的卫星图像进行比较分析，将森林砍伐检测视为一种变化检测问题。然而，现有方法仍存在显著限制：文献中评估过的模型效果并不令人满意；许多模型没有采用现代架构，如基于自注意力机制的架构；而且缺乏一种允许不同研究之间直接对比的方法论标准化。 在本文工作中，我们通过在统一的数据集中评估各种变化检测模型来解决这些问题，这些模型包括完全卷积模型以及基于变换器（Transformers）并融入了自注意力机制的网络。我们探讨了不同的预处理和后处理技术对模型效果的影响，例如根据模型预测的毁林区域大小进行滤除、纹理替换及图像增强；我们展示了这些方法可以显著提升单一模型的效果。 此外，我们测试了将评估过的不同模型组合以获得优于单个模型表现的不同策略，并达到了80.41%的F1分数，在其他最近的相关文献中这一数值具有可比性。
URL
https://arxiv.org/abs/2512.08075
