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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/127572


    Title: Analyses of Time Series InSAR Signatures for Land Cover Classification: Case studies over dense forestry areas with L-band SAR images
    Authors: 林士淵
    Lin , Shih-Yuan
    Yun, Hye-Won
    Kim , Jung-Rack
    Choi, Yun-Soo
    Contributors: 地政系
    Keywords: InSAR;land cover classification;time series
    Date: 2019-06
    Issue Date: 2019-11-26 14:41:03 (UTC+8)
    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.
    Relation: Sensors, Vol.19, No.12, pp.1-18
    Data Type: article
    DOI 連結: http://dx.doi.org/10.3390/s19122830
    DOI: 10.3390/s19122830
    Appears in Collections:[地政學系] 期刊論文

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