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Remote Sens. 2019, 11(2), 136; https://doi.org/10.3390/rs11020136

An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images

1,2
,
1
and
1,3,*
1
College of Forestry, Nanjing Forestry University, Nanjing 210037, China
2
School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China
3
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
Received: 4 December 2018 / Revised: 6 January 2019 / Accepted: 7 January 2019 / Published: 11 January 2019
(This article belongs to the Special Issue Hyperspectral Imagery for Urban Environment)
PDF [1474 KB, uploaded 11 January 2019]   |   Review Reports

Abstract

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.
Keywords: urban impervious surface; multi-feature extraction; dimensionality reduction; deep learning; hyperspectral images urban impervious surface; multi-feature extraction; dimensionality reduction; deep learning; hyperspectral images
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, Y.; Su, H.; Li, M. An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images. Remote Sens. 2019, 11, 136.

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