论文标题
高分辨率卫星图像中对象检测的合奏学习技术
Ensemble Learning techniques for object detection in high-resolution satellite images
论文作者
论文摘要
结合是一种旨在通过融合单个检测器来最大化检测性能的方法。虽然在应用于遥感的深度学习文章中很少提到，但在最近的数据科学问题（例如Kaggle）中，结合方法已被广泛用于获得高分。 少数遥感文章提及结合的主要集中于中分辨率图像和地球观察应用，例如土地使用分类，但从未在与防御相关的应用或对象检测中进行高分辨率（VHR）图像（VHR）图像。这项研究旨在审查最相关的结合技术，用于对物体进行的最相关的技术来探测该技术，并在此类技术上进行示例，以示例在此类技术中，以示例为示例，以示例示例示例，该技术的数量是一个示例，该技术的示例是一项示例，该技术的示例是一项示例的示例。区域）。
Ensembling is a method that aims to maximize the detection performance by fusing individual detectors. While rarely mentioned in deep-learning articles applied to remote sensing, ensembling methods have been widely used to achieve high scores in recent data science com-petitions, such as Kaggle. The few remote sensing articles mentioning ensembling mainly focus on mid resolution images and earth observation applications such as land use classification, but never on Very High Resolution (VHR) images for defense-related applications or object detection.This study aims at reviewing the most relevant ensembling techniques to be used for object detection on very high resolution imagery and shows an example of the value of such techniques on a relevant operational use-case (vehicle detection in desert areas).
