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View all- Allen-Dumas MXu HKurte KRastogi D(2021)Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water HazardsFrontiers in Water10.3389/frwa.2020.5623042Online publication date: 29-Jan-2021
The identification of Impervious Area from Sentinel-2 Imagery Using A Novel Spectral Spatial Residual Convolution Neural Network
Authors: 
Zhiwen Zhang, LinLin Xu, Qixin LiuAuthors Info & Claims
Zhiwen Zhang, LinLin Xu, Qixin LiuAuthors Info & ClaimsPages 88 - 91
Abstract
With the rapid increasing of urban areas, impervious surfaces play an important role as an indicator of urban development and the change of the city's environment. Due to the wide variety of materials of impervious surfaces, it is an arduous task to draw impervious surfaces. Fortunately, the Sentinel-2 satellite provides accessible multi-spectral imagery with a high spatial resolution to solve this problem. However, huge volumes of Sentinel-2 imagery produced every 5 days need a fast and accurate classifier for impervious mapping. In this paper, a novel spectral spatial residual convolution neural network (SSRCNN) has been designed to deal with the massive imagery for impervious classification with high speed and accuracy. Compared to typical algorithms, deep learning methods are more suitable in this task. The CNN demonstrates great success in image classification. In this study, a comparison between CNN and SSRCNN has been done, and the result shows that the SSRCNN model outperforms the CNN model by about 0.74 percent in terms of overall classification accuracy (OA). The use of the NVIDIA 1080Ti graphics processing unit (GPU) can improve the computational efficiency of the SSRCNN model.
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- The identification of Impervious Area from Sentinel-2 Imagery Using A Novel Spectral Spatial Residual Convolution Neural Network
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November 2019
232 pages
ISBN:9781450376754
DOI:10.1145/3373419
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- Southwest Jiaotong University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 January 2020
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ICAIP 2019
ICAIP 2019: 2019 3rd International Conference on Advances in Image Processing
November 8 - 10, 2019
Chengdu, China
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View all- Allen-Dumas MXu HKurte KRastogi D(2021)Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water HazardsFrontiers in Water10.3389/frwa.2020.5623042Online publication date: 29-Jan-2021
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