Journal
SENSORS
Volume 18, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/s18092939
Keywords
multi-source remote sensing; coherence; ground deformation
Funding
- National Key Research and Development Program of China [2017YFE0100700]
- Natural Science Foundation of China [41801337]
- Research Grants of Science and Technology Commission of Shanghai Municipality [18ZR1410800]
- Key Laboratory of Land Subsidence Monitoring and Prevention, Ministry of Land and Resources [KLLSMP201503]
- Fund of the Director of the Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University [KLGIS2017C03]
- I-AMICA project
- European Regional Development Fund (ERDF)
- Italian National Resources
- Dragon 4 ESA project [32294]
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In this work, we focused on the ocean-reclaimed lands of the Shanghai coastal region and we evidenced how, over these areas, the interferometric synthetic aperture radar (InSAR) coherence maps exhibit peculiar behavior. In particular, by analyzing a sequence of Sentinel-1 SAR InSAR coherence maps, we found a significant coherence loss over time in correspondence to the ocean-reclaimed platforms that are substantially different from the coherence loss experienced in naturally-formed regions with the same type of land cover. We have verified whether this is due to the engineering geological conditions or the soil consolidation subsidence in ocean-reclaimed region. In this work, we combine the information coming from InSAR coherence maps and the retrieved temporal decorrelation model with that obtained by using optical Sentinel-2 data, and we performed land cover classification analyses in the zone of the Pudong International Airport. To estimate the accuracy of utilizing InSAR coherence information for land cover classification, in particular, we have analyzed what causes the difference of the InSAR coherence loss with the same type of land cover. The presented results show that the coherence models can be useful to distinguish roads and buildings, thus enhancing the accuracy of land cover classification compared with that allowable by using only Sentinel-2 data. In particular, the accuracy of classification increases from 75% to 86%.
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