Mapping Urban Impervious Surface With an Unsupervised Approach Using Interferometric Coherence of SAR Images

Impervious surface is significant in hydrology, urban management, ecology, and other research areas. Therefore, extracting impervious surface is crucial to understanding the change of environment and ecosystem. However, previous supervised classification methods usually rely on comprehensive trainin...

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Main Authors: Xindan Liang, Yinyi Lin, Hongsheng Zhang
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9709101/
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author Xindan Liang
Yinyi Lin
Hongsheng Zhang
author_facet Xindan Liang
Yinyi Lin
Hongsheng Zhang
author_sort Xindan Liang
collection DOAJ
description Impervious surface is significant in hydrology, urban management, ecology, and other research areas. Therefore, extracting impervious surface is crucial to understanding the change of environment and ecosystem. However, previous supervised classification methods usually rely on comprehensive training samples and human experiences. The article on automatic and efficient impervious surface extraction is still underexplored. In this study, we investigated the potential of using interferometric synthetic aperture radar technology for unsupervised urban impervious surface (UIS) mapping. A total 136 coherence maps of Hong Kong with different perpendicular and temporal baselines were used to classify UIS and non-UIS through setting different coherence thresholds. We proposed a new method, entitled interferometric coherence thresholding method, which can achieve high classification accuracy using coherence map. The result illustrates: first, using a threshold of 0.4, nearly 90% of images achieve an overall accuracy of over 80%. The highest one reaches 88.25%, which is much higher than using K-means and ISODATA method; second, small temporal baselines (12 and 24 days) are likely to reduce the classification accuracy; third, using optimal classification threshold, the coherence of two SAR images would not bring large impact on classification results.
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spelling doaj.art-7793d9b6e64643a2a0de1d7ef7ce4f772022-12-22T01:47:24ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01152734274410.1109/JSTARS.2022.31498139709101Mapping Urban Impervious Surface With an Unsupervised Approach Using Interferometric Coherence of SAR ImagesXindan Liang0https://orcid.org/0000-0001-5147-2428Yinyi Lin1https://orcid.org/0000-0002-8769-8506Hongsheng Zhang2https://orcid.org/0000-0002-6135-9442Department of Geography, The University of Hong Kong, Hong KongDepartment of Geography, The University of Hong Kong, Hong KongDepartment of Geography, The University of Hong Kong, Hong KongImpervious surface is significant in hydrology, urban management, ecology, and other research areas. Therefore, extracting impervious surface is crucial to understanding the change of environment and ecosystem. However, previous supervised classification methods usually rely on comprehensive training samples and human experiences. The article on automatic and efficient impervious surface extraction is still underexplored. In this study, we investigated the potential of using interferometric synthetic aperture radar technology for unsupervised urban impervious surface (UIS) mapping. A total 136 coherence maps of Hong Kong with different perpendicular and temporal baselines were used to classify UIS and non-UIS through setting different coherence thresholds. We proposed a new method, entitled interferometric coherence thresholding method, which can achieve high classification accuracy using coherence map. The result illustrates: first, using a threshold of 0.4, nearly 90% of images achieve an overall accuracy of over 80%. The highest one reaches 88.25%, which is much higher than using K-means and ISODATA method; second, small temporal baselines (12 and 24 days) are likely to reduce the classification accuracy; third, using optimal classification threshold, the coherence of two SAR images would not bring large impact on classification results.https://ieeexplore.ieee.org/document/9709101/Coherenceinterferometric synthetic aperture radar (InSAR)spatial-temporal baselineunsupervised classificationurban impervious surface (UIS)
spellingShingle Xindan Liang
Yinyi Lin
Hongsheng Zhang
Mapping Urban Impervious Surface With an Unsupervised Approach Using Interferometric Coherence of SAR Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Coherence
interferometric synthetic aperture radar (InSAR)
spatial-temporal baseline
unsupervised classification
urban impervious surface (UIS)
title Mapping Urban Impervious Surface With an Unsupervised Approach Using Interferometric Coherence of SAR Images
title_full Mapping Urban Impervious Surface With an Unsupervised Approach Using Interferometric Coherence of SAR Images
title_fullStr Mapping Urban Impervious Surface With an Unsupervised Approach Using Interferometric Coherence of SAR Images
title_full_unstemmed Mapping Urban Impervious Surface With an Unsupervised Approach Using Interferometric Coherence of SAR Images
title_short Mapping Urban Impervious Surface With an Unsupervised Approach Using Interferometric Coherence of SAR Images
title_sort mapping urban impervious surface with an unsupervised approach using interferometric coherence of sar images
topic Coherence
interferometric synthetic aperture radar (InSAR)
spatial-temporal baseline
unsupervised classification
urban impervious surface (UIS)
url https://ieeexplore.ieee.org/document/9709101/
work_keys_str_mv AT xindanliang mappingurbanimpervioussurfacewithanunsupervisedapproachusinginterferometriccoherenceofsarimages
AT yinyilin mappingurbanimpervioussurfacewithanunsupervisedapproachusinginterferometriccoherenceofsarimages
AT hongshengzhang mappingurbanimpervioussurfacewithanunsupervisedapproachusinginterferometriccoherenceofsarimages