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Open AccessEditor’s ChoiceArticle

How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?

1
School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester M1 5GD, UK
2
School of Medicine, Tongji University, Shanghai 200092, China
3
Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK
4
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 417; https://doi.org/10.3390/rs12030417
Received: 27 November 2019 / Revised: 15 January 2020 / Accepted: 17 January 2020 / Published: 28 January 2020
Land cover information plays an important role in mapping ecological and environmental changes in Earth’s diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from Space. Although the availability of high-resolution remote sensing imagery increases significantly every year, traditional land cover analysis approaches based on pixel and object levels are not optimal. Recent advancement in deep learning has achieved remarkable success on image recognition field and has shown potential in high spatial resolution remote sensing applications, including classification and object detection. In this paper, a comprehensive review on land cover classification and object detection approaches using high resolution imagery is provided. Through two case studies, we demonstrated the applications of the state-of-the-art deep learning models to high spatial resolution remote sensing data for land cover classification and object detection and evaluated their performances against traditional approaches. For a land cover classification task, the deep-learning-based methods provide an end-to-end solution by using both spatial and spectral information. They have shown better performance than the traditional pixel-based method, especially for the categories of different vegetation. For an objective detection task, the deep-learning-based object detection method achieved more than 98% accuracy in a large area; its high accuracy and efficiency could relieve the burden of the traditional, labour-intensive method. However, considering the diversity of remote sensing data, more training datasets are required in order to improve the generalisation and the robustness of deep learning-based models. View Full-Text
Keywords: remote sensing; land cover; deep learning; computer vision remote sensing; land cover; deep learning; computer vision
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MDPI and ACS Style

Zhang, X.; Han, L.; Han, L.; Zhu, L. How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery? Remote Sens. 2020, 12, 417. https://doi.org/10.3390/rs12030417

AMA Style

Zhang X, Han L, Han L, Zhu L. How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery? Remote Sensing. 2020; 12(3):417. https://doi.org/10.3390/rs12030417

Chicago/Turabian Style

Zhang, Xin; Han, Liangxiu; Han, Lianghao; Zhu, Liang. 2020. "How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?" Remote Sens. 12, no. 3: 417. https://doi.org/10.3390/rs12030417

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