Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicles (UAV) based applications have dominated aerial sensing research. However, a literature revision that combines both "deep learning" and "UAV remote sensing" thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published material and evaluated their characteristics regarding application, sensor, and technique used. We relate how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. Our revision consists of a friendly-approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.

翻译：深度神经网络（DNNs）凭借其从数据中学习表示的超凡能力，为图像、时间序列、自然语言、音频、视频等多种数据处理带来了重要突破。在遥感领域，已有综述和文献回顾专门针对深度神经网络算法的应用，试图总结其子领域产生的大量信息。近年来，基于无人机的应用主导了航空遥感研究。然而，目前尚缺乏同时涵盖"深度学习"与"无人机遥感"主题的文献综述。本工作的动因在于，对应用于无人机影像的深度学习基本原理进行全面综述。我们重点描述了近期利用无人机采集数据的应用中所使用的分类与回归技术。为此，我们查阅了发表于国际科学期刊数据库的共计232篇论文，收集已发表材料并评估其在应用方向、传感器类型及所用技术方面的特征。我们阐述了深度学习如何展现其优异的性能，并具备处理无人机影像数据相关任务的潜力。最后，我们展望了未来发展方向，评述了无人机遥感领域中值得探索的深度学习前沿路径。本综述以友好的方式介绍、评述并总结遥感各子领域（归纳为环境、城市和农业场景）中基于深度神经网络的无人机影像应用的最新进展。