Deep Belief Network for Feature Extraction of Urban Artificial Targets
Joint Authors
Dai, Xiaoai
Cheng, Junying
Gao, Yu
Guo, Shouheng
Yang, Xingping
Xu, Xiaoqian
Cen, Yi
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-30
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Reducing the dimension of the hyperspectral image data can directly reduce the redundancy of the data, thus improving the accuracy of hyperspectral image classification.
In this paper, the deep belief network algorithm in the theory of deep learning is introduced to extract the in-depth features of the imaging spectral image data.
Firstly, the original data is mapped to feature space by unsupervised learning methods through the Restricted Boltzmann Machine (RBM).
Then, a deep belief network will be formed by superimposed multiple Restricted Boltzmann Machines and training the model parameters by using the greedy algorithm layer by layer.
At the same time, as the objective of data dimensionality reduction is achieved, the underground feature construction of the original data will be formed.
The final step is to connect the depth features of the output to the Softmax regression classifier to complete the fine-tuning (FT) of the model and the final classification.
Experiments using imaging spectral data showing the in-depth features extracted by the profound belief network algorithm have better robustness and separability.
It can significantly improve the classification accuracy and has a good application prospect in hyperspectral image information extraction.
American Psychological Association (APA)
Dai, Xiaoai& Cheng, Junying& Gao, Yu& Guo, Shouheng& Yang, Xingping& Xu, Xiaoqian…[et al.]. 2020. Deep Belief Network for Feature Extraction of Urban Artificial Targets. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1193798
Modern Language Association (MLA)
Dai, Xiaoai…[et al.]. Deep Belief Network for Feature Extraction of Urban Artificial Targets. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1193798
American Medical Association (AMA)
Dai, Xiaoai& Cheng, Junying& Gao, Yu& Guo, Shouheng& Yang, Xingping& Xu, Xiaoqian…[et al.]. Deep Belief Network for Feature Extraction of Urban Artificial Targets. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1193798
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193798