An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images
Hyperspectral images (HSIs) provide unique capabilities for urban impervious surfaces (UIS) extraction. This paper proposes a multi-feature extraction model (MFEM) for UIS detection from HSIs. The model is based on a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor emb...
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doaj-bbbbc65ac83f4b3f9bfb55c1364278d42020-11-25T00:29:56ZengMDPI AGRemote Sensing2072-42922019-01-0111213610.3390/rs11020136rs11020136An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral ImagesYuliang Wang0Huiyi Su1Mingshi Li2College of Forestry, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Forestry, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Forestry, Nanjing Forestry University, Nanjing 210037, ChinaHyperspectral images (HSIs) provide unique capabilities for urban impervious surfaces (UIS) extraction. This paper proposes a multi-feature extraction model (MFEM) for UIS detection from HSIs. The model is based on a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding (t-SNE), and the deep learning method convolutional deep belief networks (CDBNs). We improved the two methods to create a novel MFEM consisting of improved t-SNE, deep compression CDBNs (d-CDBNs), and a logistic regression classifier. The improved t-SNE method provides dimensionality reduction and spectral feature extraction from the original HSIs and the d-CDBNs algorithm extracts spatial feature and edges using the reduced dimensional datasets. Finally, the extracted features are combined into multi-feature for the impervious surface detection using the logistic regression classifier. After comparing with the commonly used methods, the current experimental results demonstrate that the proposed MFEM model provides better performance for UIS extraction and detection from HSIs.http://www.mdpi.com/2072-4292/11/2/136urban impervious surfacemulti-feature extractiondimensionality reductiondeep learninghyperspectral images |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuliang Wang Huiyi Su Mingshi Li |
spellingShingle |
Yuliang Wang Huiyi Su Mingshi Li An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images Remote Sensing urban impervious surface multi-feature extraction dimensionality reduction deep learning hyperspectral images |
author_facet |
Yuliang Wang Huiyi Su Mingshi Li |
author_sort |
Yuliang Wang |
title |
An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images |
title_short |
An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images |
title_full |
An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images |
title_fullStr |
An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images |
title_full_unstemmed |
An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images |
title_sort |
improved model based detection of urban impervious surfaces using multiple features extracted from rosis-3 hyperspectral images |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-01-01 |
description |
Hyperspectral images (HSIs) provide unique capabilities for urban impervious surfaces (UIS) extraction. This paper proposes a multi-feature extraction model (MFEM) for UIS detection from HSIs. The model is based on a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding (t-SNE), and the deep learning method convolutional deep belief networks (CDBNs). We improved the two methods to create a novel MFEM consisting of improved t-SNE, deep compression CDBNs (d-CDBNs), and a logistic regression classifier. The improved t-SNE method provides dimensionality reduction and spectral feature extraction from the original HSIs and the d-CDBNs algorithm extracts spatial feature and edges using the reduced dimensional datasets. Finally, the extracted features are combined into multi-feature for the impervious surface detection using the logistic regression classifier. After comparing with the commonly used methods, the current experimental results demonstrate that the proposed MFEM model provides better performance for UIS extraction and detection from HSIs. |
topic |
urban impervious surface multi-feature extraction dimensionality reduction deep learning hyperspectral images |
url |
http://www.mdpi.com/2072-4292/11/2/136 |
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