Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks
Joint Authors
Dou, Yong
Niu, Xin
Xu, Jiaqing
Lv, Qi
Xu, Jinbo
Xia, Fei
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-07-29
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Land use and land cover (LULC) mapping in urban areas is one of the core applications in remote sensing, and it plays an important role in modern urban planning and management.
Deep learning is springing up in the field of machine learning recently.
By mimicking the hierarchical structure of the human brain, deep learning can gradually extract features from lower level to higher level.
The Deep Belief Networks (DBN) model is a widely investigated and deployed deep learning architecture.
It combines the advantages of unsupervised and supervised learning and can archive good classification performance.
This study proposes a classification approach based on the DBN model for detailed urban mapping using polarimetric synthetic aperture radar (PolSAR) data.
Through the DBN model, effective contextual mapping features can be automatically extracted from the PolSAR data to improve the classification performance.
Two-date high-resolution RADARSAT-2 PolSAR data over the Great Toronto Area were used for evaluation.
Comparisons with the support vector machine (SVM), conventional neural networks (NN), and stochastic Expectation-Maximization (SEM) were conducted to assess the potential of the DBN-based classification approach.
Experimental results show that the DBN-based method outperforms three other approaches and produces homogenous mapping results with preserved shape details.
American Psychological Association (APA)
Lv, Qi& Dou, Yong& Niu, Xin& Xu, Jiaqing& Xu, Jinbo& Xia, Fei. 2015. Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks. Journal of Sensors،Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1070148
Modern Language Association (MLA)
Lv, Qi…[et al.]. Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks. Journal of Sensors No. 2015 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1070148
American Medical Association (AMA)
Lv, Qi& Dou, Yong& Niu, Xin& Xu, Jiaqing& Xu, Jinbo& Xia, Fei. Urban Land Use and Land Cover Classification Using Remotely Sensed SAR Data through Deep Belief Networks. Journal of Sensors. 2015. Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1070148
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1070148