Urban built-up areas extraction by the multiscale stacked denoising autoencoder technique Article Swipe
Xiaofei Mi
,
Weijia Cao
,
Jian Yang
,
Zhenghuan Li
,
Yazhou Zhang
,
Qianjing Li
,
Zhensheng Sun
,
Yulin Zhan
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1117/1.jrs.14.032607
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1117/1.jrs.14.032607
Stacked denoising autoencoder (SDAE) model has a strong feature learning ability and has shown great success in the classification of remote sensing images. However, built-up area (BUA) information is easily interfered with by broken rocks, bare land, and other features with similar spectral features. SDAEs are vulnerable to broken and similar features in the image. We propose a multiscale SDAE model to overcome these problems, which can extract BUA features in different scales and recognize the type of land object from multiple scales. The model effectively improves the recognition rate of BUA. The experimental results show that our algorithm can resist the disturbance information, and the classification accuracies are better than support vector machine, backpropagation, random forests, and SDAE. Then we investigate an application in Wuhan (China) metropolitan area analysis with the classification results of our algorithm. The range of the metropolitan area is 1.5-h isochronous circle calculated by Tencent map big data and is divided into three layers: core metropolitan area, subcore metropolitan area, and daily metropolitan. Finally, from the comprehensive statistical data and traffic data, we know that the Wuhan metropolitan area has a “target-shaped” distribution structure radiating outward from the core metropolitan area. It includes five metropolitan development corridors: Wuhan–Huanggang, Wuhan–Xiaogan–Suizhou, Wuhan–Ezhou–Huangshi, Wuhan–Xiantao–Tianmen, and Wuhan–Xianan–Chibi. The corridor is of great significance to the development of metropolitan areas.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1117/1.jrs.14.032607
- https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-14/issue-3/032607/Urban-built-up-areas-extraction-by-the-multiscale-stacked-denoising/10.1117/1.JRS.14.032607.pdf?SSO=1
- OA Status
- bronze
- Cited By
- 2
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3005073935
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- OpenAlex ID
-
https://openalex.org/W3005073935Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1117/1.jrs.14.032607Digital Object Identifier
- Title
-
Urban built-up areas extraction by the multiscale stacked denoising autoencoder techniqueWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-02-05Full publication date if available
- Authors
-
Xiaofei Mi, Weijia Cao, Jian Yang, Zhenghuan Li, Yazhou Zhang, Qianjing Li, Zhensheng Sun, Yulin ZhanList of authors in order
- Landing page
-
https://doi.org/10.1117/1.jrs.14.032607Publisher landing page
- PDF URL
-
https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-14/issue-3/032607/Urban-built-up-areas-extraction-by-the-multiscale-stacked-denoising/10.1117/1.JRS.14.032607.pdf?SSO=1Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-14/issue-3/032607/Urban-built-up-areas-extraction-by-the-multiscale-stacked-denoising/10.1117/1.JRS.14.032607.pdf?SSO=1Direct OA link when available
- Concepts
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Metropolitan area, Computer science, Feature extraction, Autoencoder, Artificial intelligence, Remote sensing, Deep learning, Backpropagation, Pattern recognition (psychology), Geography, Artificial neural network, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2023: 2Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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