The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Deep supervised learning for hyperspectral data classification through convolutional neural networks
Abstract
Spectral observations along the spectrum in many narrow spectral bands through hyperspectral imaging provides valuable information towards material and object recognition, which can be consider as a classification task. Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on the construction of complex handcrafted features. However, it is rarely known which features are important for the problem at hand. In contrast to these approaches, we propose a deep learning based classification method that hierarchically constructs high-level features in an automated way. Our method exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task. Experimental results and quantitative validation on widely used datasets showcasing the potential of the developed approach for accurate hyperspectral data classification.
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Cites
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Deep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679
Neural Networks: Tricks of the Trade: Second Edition
(2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8 · Cited by 2,116
Cited by
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400
Deep Learning for Hyperspectral Image Classification: An Overview
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2907932 · Cited by 2,007
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
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746
Surface Water Mapping by Deep Learning
Isikdogan, F., Bovik, A. C., & Passalacqua, P. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2735443 · Cited by 292
Metadata
- Authors
- Makantasis, K., Karantzalos, K., Doulamis, A., & Doulamis, N.
- Venue
- 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
- Publisher
- IEEE
- Published
- 2015-07
- Pages
- 4959-4962
- OpenAlex
- W1966580635
- Cited by
- 1,011
- References
- 5
- FWCI
- 84.57
- Type
- conference paper
- Language
- en
- Open access
- closed
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