Monitoring Glacier Changes Using Multitemporal Multipolarization SAR Images

This paper presents a processing chain for the change detection of Arctic glaciers from multitemporal multipolarization synthetic aperture radar (SAR) images. We produce terrain-corrected multilook complex covariance data by including the effects of topography on both geolocation and SAR radiometry...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 52; číslo 6; s. 3729 - 3741
Hlavní autoři: Akbari, Vahid, Doulgeris, Anthony P., Eltoft, Torbjorn
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Backscatter
Change detection
Covariance matrices
Data models
Glaciers
Image detection
Matrix log-cumulant diagram
multilook product model
polarimetric synthetic aperture radar (PolSAR)
postclassification change detection
Radar
radiometric terrain correction (RTC)
Radiometry
Scattering
Segmentation
Signatures
Slopes
Synthetic aperture radar
unsupervised contextual non-Gaussian segmentation
Vectors
Arctic region
Norway, Svalbard
PN, Arctic
Norway, Svalbard, Spitsbergen, Kongsvegen
multilook product model
postclassification change detection
Matrix log-cumulant diagram
polarimetric synthetic aperture radar (PolSAR)
radiometric terrain correction (RTC)
unsupervised contextual non-Gaussian segmentation
ISSN:0196-2892, 1558-0644
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Shrnutí:This paper presents a processing chain for the change detection of Arctic glaciers from multitemporal multipolarization synthetic aperture radar (SAR) images. We produce terrain-corrected multilook complex covariance data by including the effects of topography on both geolocation and SAR radiometry as well as azimuth slope variations on polarization signature. An unsupervised contextual non-Gaussian clustering algorithm is employed for the segmentation of each terrain-corrected polarimetric SAR image and subsequently labeled with the aid of ground-truth data into glacier facies. We demonstrate the consistency of the segmentation algorithm by characterizing the expected random error level for different SAR acquisition conditions. This allows us to determine whether an observed variation is statistically significant and therefore can be used for the postclassification change detection of Arctic glaciers. Subsequently, the average classified images of succeeding years are compared, and changes are identified as the detected differences in the location of boundaries between glacier facies. In the current analysis, a series of dual-polarization C-band ENVISAT ASAR images over the Kongsvegen glacier, Svalbard, is used for demonstration.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2013.2275203