Abstract
Transfer learning is a new topic in machine learning. Psychology holds that the process of learning knowledge from one to the other is a process of transfer learning. Transfer learning is different from machine learning which has to satisfy the following two conditions: (1) The training samples and testing samples must be in the same feature spaces. (2) There must be enough training samples to obtain an excellent training model. Because of the ability of transfer learning to solve problems with small samples and the ability to use historical auxiliary models to solve new problems, it is introduced in remote sensing image analysis. At first, this paper introduces some basic knowledge of transfer learning and enumerates some basic research examples. The research content of this paper mainly involves several problems based on transfer learning, such as target detection and recognition, image classification, etc.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (61771154) and the Fundamental Research Funds for the Central Universities (HEUCFG201830).
Meantime, all the authors declare that there is no conflict of interests regarding the publication of this article.
We gratefully thank of very useful discussions of reviewers.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wu, R., Li, Y., Han, H., Chen, X., Lin, Y. (2019). Remote Sensing Image Analysis Based on Transfer Learning: A Survey. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_45
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DOI: https://doi.org/10.1007/978-3-030-19086-6_45
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