利用变化检测模型识别亚马逊雨林的毁林区域
Identification of Deforestation Areas in the Amazon Rainforest Using Change Detection Models
AI总结：
本研究通过统一数据集评估多种变化检测模型，结合预处理和后处理技术提升模型性能，达到80.41%的F1分数。
摘要：
亚马逊雨林的保护是全球应对气候变化、保护生物多样性和保障原住民文化的重要优先事项。巴西法律亚马逊地区毁林卫星监测项目（PRODES），由国家空间研究院（INPE）发起，是这一努力中的关键举措，每年监测亚马逊及其他巴西生物群落的毁林区域。最近，研究人员利用PRODES数据开发了机器学习模型，通过多时相卫星图像的对比分析，将毁林检测视为变化检测问题。然而，现有方法存在显著局限：文献中评估的模型仍表现出不满意的性能，许多模型未采用现代架构，如基于自注意力机制的架构，且缺乏方法学标准化，无法直接比较不同研究的结果。在本工作中，我们通过统一的数据集评估了各种变化检测模型，包括全卷积模型和基于Transformer的自注意力机制网络。我们研究了不同的预处理和后处理技术，如根据连通组件大小过滤模型预测的毁林区域、纹理替换和图像增强；我们证明这些方法可以显著提高单个模型的性能。此外，我们测试了不同的模型组合策略，以获得优于单独模型的结果，达到了80.41%的F1分数，这一数值与其他近期文献中的工作相当。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
