Abstract:
The surface velocity of glaciers is important for the estimation of ice discharge, to understand glacier
dynamics and its response to climate change, etc. However, the glacier surface velocity data collected using
conventional field measurements is insufficient. Therefore, remote sensing based techniques are being used extensively
in glaciological studies as it offers the comprehensive and repetitive monitoring in a cost effective manner. Recently,
Co-registration of Optically Sensed Images and Correlation (COSI-Corr) tool (Leprince et al., 2007) which is based on
image matching method of optical images has gained popularity for the estimation of glacier surface velocity and has
proved to be robust and effective. For denoising the glacier surface velocity estimates, Non-Local Means (NLM) filter
has been incorporated in COSI-Corr. However, to discard/replace erroneous values, user has to manually define the
threshold to discard/replace erroneous values. Although the NLM filter in COSI-Corr gives smooth estimates, but it has
been observed that the filtered values deviate from the original ones. Therefore, the main purpose of this study has been
to find an optimal filter for obtaining smooth estimates while preserving the original. Various spatial domain filters such
as mean filter, low-pass filter, adaptive filter and statistical filters have been applied and evaluated in this study to
obtain smooth glacier surface velocity estimates. The comparative evaluation of these filters showed that the application
of statistical filter with multipliers 1, 2 and 4 in sequential order followed by 3 x 3 mean filter provided close match
with the original estimates.
