Environmental protection is a fundamental policy in many countries, where the vehicle emission pollution turns to be outstanding as a main component of pollutions in environmental monitoring. Remote sensing technology has been widely used on vehicle emission detection recently and this is mainly due to the fast speed, reality, and large scale of the detection data retrieved from remote sensing methods. In the remote sensing process, the information about the fuel type and registration time of new cars and nonlocal registered vehicles usually cannot be accessed, leading to the failure in assessing vehicle pollution situations directly by analyzing emission pollutants. To handle this problem, this paper adopts data mining methods to analyze the remote sensing data to predict fuel type and registration time. This paper takes full use of decision tree, random forest, AdaBoost, XgBoost, and their fusion models to successfully make precise prediction for these two essential information and further employ them to an essential application: vehicle emission evaluation.

中文翻译：

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数据驱动方法中的车辆排放检测

环境保护是许多国家的一项基本政策，在这些国家中，机动车排放污染已成为环境监测中污染的主要组成部分。近年来，遥感技术已广泛用于车辆排放物的检测，这主要是由于从遥感方法检索到的检测数据的快速，现实和大规模的缘故。在遥感过程中，通常无法访问有关新车和非本地注册车辆的燃料类型和注册时间的信息，从而导致无法直接通过分析排放污染物来评估车辆污染状况。针对该问题，本文采用数据挖掘的方法对遥感数据进行分析，以预测燃料类型和配准时间。本文充分利用了决策树，