Abstract
This chapter presents a method for post-classification change detection in Arctic glaciers from multi-polarization synthetic aperture radar images. We produce terrain corrected multilook complex (MLC) 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 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 post-classification 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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Akbari, V., Doulgeris, A.P., Eltoft, T. (2016). Post-classification Change Detection in Arctic Glaciers by Multi-polarization SAR. In: Ban, Y. (eds) Multitemporal Remote Sensing. Remote Sensing and Digital Image Processing, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-47037-5_7
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DOI: https://doi.org/10.1007/978-3-319-47037-5_7
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