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The application of artificial neural networks to the analysis of remotely sensed data
Abstract
Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely sensed data. Although significant progress has been made in image classification based upon neural networks, a number of issues remain to be resolved. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying ANNs, including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. The limitations and crucial issues relating to the application of the neural network approach are discussed. A brief review of the implementation of ANNs in some of the most popular image processing software packages is presented. Finally, we discuss the application perspectives of neural networks in remote sensing image analysis.
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Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015) · Ore Geology Reviews · doi:10.1016/j.oregeorev.2015.01.001 · Cited by 1,576
Convolutional neural networks for large-scale remote-sensing image classification
Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2612821 · Cited by 1,114
Joint deep learning for land cover and land use classification
Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., & Atkinson, P. M. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.11.014 · Cited by 451
A patch-based convolutional neural network for remote sensing image classification
Sharma, A., Liu, X., Yang, X., & Shi, D. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.07.017 · Cited by 260
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Nor, A. N. M., Corstanje, R., Harris, J. A., & Brewer, T. (2017) · Ecological Indicators · doi:10.1016/j.ecolind.2017.05.031 · Cited by 223
Metadata
- Authors
- Mas, J. F., & Flores, J. J.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2007-12-21
- Volume
- 29
- Issue
- 3
- Pages
- 617-663
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2079454091
- Cited by
- 616
- References
- 152
- FWCI
- 20.03
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
