Eyes in the Skies: A Data-driven Fusion Approach to Identifying Drug Crops from Remote Sensing Images Dataset

Automatic classification of sensitive content in remote sensing images, such as drug crop sites, is a promising task as it can aid law-enforcement institutions fighting illegal drug dealers worldwide, while, at the same time, it can help monitoring legalized crops in countries that regulate them. However, existing art on detecting drug crops from remote sensing images is limited in some key factors not taking full advantage of the available hyperspectral info for analysis. In this paper, departing from these methods, we propose a data-driven ensemble method to detect drug sites from remote sensing images. Our method comprises different Convolutional Neural Network architectures applied to distinct image representations, which are able to represent complementary characterizations of such crops. To validate the proposed approach, we considered in our experiments a dataset containing Cannabis Sativa crops, spotted by police operations in a Brazilian region called the Marijuana Polygon. Results in this dataset show that our ensemble approach outperforms other data-driven and feature-engineering methods in a real-world experimental setup, in which unbalanced samples are present and acquisitions from different places are used for training and testing the methods, highlighting the promising use of this solution to aid police operations in detecting and collecting evidence of such sensitive content properly.

自动对遥感图像中的敏感内容进行分类，如毒品作物种植地，是一项具有广阔前景的任务，因为它有助于全球执法机构打击非法毒品贩运，同时，它还能协助监管合法作物的国家监控其种植情况。然而，现有关于从遥感图像中检测毒品作物的技术，在关键因素上存在局限性，未能充分利用可用的高光谱信息进行深入分析。在本研究中，我们跳出传统方法，提出一种基于数据驱动的集成方法，用于从遥感图像中检测毒品种植地。该方法由应用于不同图像表征的不同卷积神经网络架构组成，能够表征此类作物的互补特征。为验证所提出的方法，我们在实验中考虑了一个包含大麻（Cannabis Sativa）作物数据集，该数据集由巴西马托格罗索州（Mato Grosso）的“大麻多边形”地区警方行动发现。该数据集的结果表明，我们的集成方法在现实世界的实验设置中优于其他基于数据和特征工程的方法，其中存在不平衡样本，且来自不同地点的采集数据用于训练和测试这些方法，凸显了该解决方案在协助警方检测和收集此类敏感内容证据方面的巨大潜力。