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Cai, Y., Guan, K., Peng, J., Wang, S., Seifert, C., Wardlow, B. and Li, Z. (2018) A High-Performance and In-Season Classification System of Field-Level Crop Types Using Time-Series Landsat Data and a Machine Learning Approach. Remote Sensing of Environment, 210, 35-47.
https://doi.org/10.1016/j.rse.2018.02.045

has been cited by the following article:

• TITLE: A Review of Researches on Deep Learning in Remote Sensing Application

  AUTHORS: Ming Zhu, Yongning He, Qingyu He

  KEYWORDS: Deep Learning, Remote Sensing Application, CNN, Land Cover Classification, Object Detection, Change Detection

  JOURNAL NAME: International Journal of Geosciences, Vol.10 No.1, January 10, 2019

  ABSTRACT: In recent years, deep learning has been widely used in the field of image understanding and made breakthroughs research progress in image understanding. Because remote sensing application and image understanding are inseparable, researchers have carried out a lot of research on the application of deep learning in remote sensing field, and extended the deep learning method to various application fields of remote sensing. This paper summarizes the basic principles of deep learning and its research progress and typical applications in remote sensing, introduces the current main deep learning model and its development history, focuses on the analysis and elaboration of the research status of deep learning in remote sensing image classification, object detection and change detection, and on this basis, summarizes the typical applications and their application effects. Finally, according to the current application of deep learning in remote sensing, the main problems and future development directions are summarized.