论文标题
使用RGB和秘鲁雨林中的多光谱图像对小水体进行半监督的变化检测
Semi-supervised Change Detection of Small Water Bodies Using RGB and Multispectral Images in Peruvian Rainforests
论文作者
论文摘要
手工和小规模的黄金开采（ASGM）是许多家庭的重要收入来源，但它可以产生巨大的社会和环境影响，尤其是在发展中国家的雨林中。 Sentinel-2卫星收集了多光谱图像，可用于检测水位和质量的变化，这表明采矿地点的位置。这项工作着重于对秘鲁亚马逊雨林中ASGM活动的认可。我们根据支持向量机（SVM）测试了几个半监督分类器，以检测Madre de Dios地区从2019年到2021年的水体变化，这是ASGM活动的全球热点之一。实验表明，基于SVM的模型可以实现RGB（使用Cohen的$κ$ 0.49）和6通道图像（使用Cohen的$κ$ 0.71）的合理性能，并具有非常有限的注释。还分析了合并实验室色彩空间的功效。
Artisanal and Small-scale Gold Mining (ASGM) is an important source of income for many households, but it can have large social and environmental effects, especially in rainforests of developing countries. The Sentinel-2 satellites collect multispectral images that can be used for the purpose of detecting changes in water extent and quality which indicates the locations of mining sites. This work focuses on the recognition of ASGM activities in Peruvian Amazon rainforests. We tested several semi-supervised classifiers based on Support Vector Machines (SVMs) to detect the changes of water bodies from 2019 to 2021 in the Madre de Dios region, which is one of the global hotspots of ASGM activities. Experiments show that SVM-based models can achieve reasonable performance for both RGB (using Cohen's $κ$ 0.49) and 6-channel images (using Cohen's $κ$ 0.71) with very limited annotations. The efficacy of incorporating Lab color space for change detection is analyzed as well.
