遥感深度学习研究进展

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摘要翻译：
站在向数据密集型科学的范式转变之际，机器学习技术变得越来越重要。特别是，深度学习作为该领域的一个重大突破，已经在许多领域被证明是一个极其强大的工具。我们是否将深度学习视为所有人的关键？或者，我们应该抵制‘黑箱’解决方案吗？在遥感界有争议的意见。在这篇文章中，我们分析了使用深度学习进行遥感数据分析的挑战，回顾了最近的进展，并提供了资源，使遥感中的深度学习变得非常简单。更重要的是，我们倡导遥感科学家将其专长带入深度学习，并将其作为一种隐含的通用模型，以应对前所未有的大规模有影响力的挑战，如气候变化和城市化。
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英文标题：
《Deep learning in remote sensing: a review》
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作者：
Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang,
  Feng Xu, Friedrich Fraundorfer
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最新提交年份：
2017
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.
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PDF链接：
https://arxiv.org/pdf/1710.03959

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关键词：深度学习 研究进展 Construction Breakthrough Mathematical 提供 data intensive 范式 science