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      Analyses of Time Series InSAR Signatures for Land Cover Classification: Case Studies over Dense Forestry Areas with L-Band SAR Images

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          Abstract

          As demonstrated in prior studies, InSAR holds great potential for land cover classification, especially considering its wide coverage and transparency to climatic conditions. In addition to features such as backscattering coefficient and phase coherence, the temporal migration in InSAR signatures provides information that is capable of discriminating types of land cover in target area. The exploitation of InSAR signatures was expected to provide merits to trace land cover change in extensive areas; however, the extraction of suitable features from InSAR signatures was a challenging task. Combining time series amplitudes and phase coherences through linear and nonlinear compressions, we showed that the InSAR signatures could be extracted and transformed into reliable classification features for interpreting land cover types. The prototype was tested in mountainous areas that were covered with a dense vegetation canopy. It was demonstrated that InSAR time series signature analyses reliably identified land cover types and also recognized tracing of temporal land cover change. Based on the robustness of the developed scheme against the temporal noise components and the availability of advanced spatial and temporal resolution SAR data, classification of finer land cover types and identification of stable scatterers for InSAR time series techniques can be expected. The advanced spatial and temporal resolution of future SAR assets combining the scheme in this study can be applicable for various important applications including global land cover changes monitoring.

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          Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry

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            Tropical Forest Biomass Density Estimation Using JERS-1 SAR: Seasonal Variation, Confidence Limits, and Application to Image Mosaics

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              Retrieval of vegetation parameters with SAR interferometry

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                25 June 2019
                June 2019
                : 19
                : 12
                : 2830
                Affiliations
                [1 ]Department of Geoinformatics, University of Seoul, Seoulsiripdaero 163, Dongdaemum-gu, Seoul 02504, Korea; hwyun0221@ 123456korea.kr (H.-W.Y.); kjrr001@ 123456gmail.com (J.-R.K.); choiys@ 123456uos.ac.kr (Y.-S.C.)
                [2 ]Disaster Information Research Division, National Disaster Management Research Institute, 365 Jongga-ro, Jung-gu, Ulsan 44538, Korea
                [3 ]Department of Land Economics, National Chengchi University, No. 64, Sec. 2, Zhinan Road, Wenshan District, Taipei 116, Taiwan
                Author notes
                [* ]Correspondence: syl@ 123456nccu.edu.tw ; Tel.: +886-2-29393091 (ext. 51651)
                Author information
                https://orcid.org/0000-0003-3494-5400
                Article
                sensors-19-02830
                10.3390/s19122830
                6631005
                31242629
                fb581309-70d1-4b04-8640-4f156e4cfc74
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 May 2019
                : 18 June 2019
                Categories
                Article

                Biomedical engineering
                insar,time series,land cover classification
                Biomedical engineering
                insar, time series, land cover classification

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