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An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. ] Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China.ORCID iD: 0000-0001-9907-0989
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0001-9692-8636
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-1369-3216
Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China..
2019 (English)In: ISPRS journal of photogrammetry and remote sensing (Print), ISSN 0924-2716, E-ISSN 1872-8235, Vol. 158, p. 50-62Article in journal (Refereed) Published
Abstract [en]

Compared with optical sensors, the all-weather and day-and-night imaging ability of Synthetic Aperture Radar (SAR) makes it competitive for burnt area mapping. This study investigates the potential of Sentinel-1 C-band SAR sensors in burnt area mapping with an implicit Radar Convolutional Burn Index (RCBI). Based on multi temporal Sentinel-1 SAR data, a convolutional networks-based classification framework is proposed to learn the RCBI for highlighting the burnt areas. We explore the mapping accuracy level that can be achieved using SAR intensity and phase information for both VV and VH polarizations. Moreover, we investigate the decorrelation of Interferometric SAR (InSAR) coherence to wildfire events using different temporal baselines. The experimental results on two recent fire events, Thomas Fire (Dec., 2017) and Carr Fire (July, 2018) in California, demonstrate that the learnt RCBI has a better potential than the classical log-ratio operator in highlighting burnt areas. By exploiting both VV and VH information, the developed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the Thomas Fire and Carr Fire, respectively.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 158, p. 50-62
Keywords [en]
Sentinel-1 SAR, Burnt area mapping, InSAR coherence, Change detection, Fully Convolutional Networks (FCN), Radar Convolutional Burn Index (RCBI)
National Category
Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:kth:diva-266186DOI: 10.1016/j.isprsjprs.2019.09.013ISI: 000501404100005Scopus ID: 2-s2.0-85072860997OAI: oai:DiVA.org:kth-266186DiVA, id: diva2:1383548
Note

QC 20200108

Available from: 2020-01-08 Created: 2020-01-08 Last updated: 2020-01-08Bibliographically approved

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Zhang, PuzhaoNascetti, AndreaBan, Yifang

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