Glacier Surface Monitoring by Maximizing Mutual Information

Abstract

ABSTRACT:The contribution of Polarimetric Synthetic Aperture Radar (PolSAR) images compared with the single-channel SAR in terms of temporal scene characterization has been found and described to add valuable information in the literature. However, despite a number of recent studies focusing on single polarized glacier monitoring, the potential of polarimetry to estimate the surface velocity of glaciers has not been explored due to the complex mechanism of polarization through glacier/snow. In this paper, a new approach to the problem of monitoring glacier surface velocity is proposed by means of temporal PolSAR images, using a basic concept from information theory: Mutual Information (MI). The proposed polarimetric tracking method applies the MI to measure the statistical dependence between temporal polarimetric images, which is assumed to be maximal if the images are geometrically aligned. Since the proposed polarimetric tracking method is very powerful and general, it can be implemented into any kind of multivariate remote sensing data such as multi-spectral optical and single-channel SAR images. The proposed polarimetric tracking is then used to retrieve surface velocity of Aletsch glacier located in Switzerland and of Inyltshik glacier in Kyrgyzstan with two different SAR sensors; Envisat C-band (single polarized) and DLR airborne L-band (fully polarimetric) systems, respectively. The effect of number of channel (polarimetry) into tracking investigations demonstrated that the presence of snow, as expected, effects the location of the phase center in different polarization, such as glacier tracking with temporal HH compared to temporal VV channels. Shortly, a change in polarimetric signature of the scatterer can change the phase center, causing a question of how much of what I am observing is motion then penetration. In this paper, it is shown that considering the multi-channel SAR statistics, it is possible to optimize the separate these contributions.

Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring

“…In addition to apparent benefits, MMI has also been used in pattern recognition for feature selection [68] and classification [69]. Particularly in remote sensing, spatio-temporal analysis based on the MI of multi-band images has been proposed to detect change [70][71][72] and to monitor glaciers [73]. These studies reached their objective by maximizing the MI of two variables, A and B, i.e.,…”

Glacier surface velocity measure based on polarimetric tracking

“…The proposed multi-channel tracking method -as shown in [1,2]-has been successfully applied into Aletsch and Inyltshik glacier located in Switzerland and Kyrgyzstan for surface velocity measurement. Here, to validate the proposed method, we will more concentrate on fully polarimetric images at L-band in terms of application of interest.…”

“…1. In [2], the polarimetric tracking algorithm is proposed based on the maximization of the MI between temporal covariance matrices.…”

“…However, the potential of fully polarimetric data for glacier monitoring has not been fully investigated yet. Recently, a new polarimetric tracking for glacier surface monitoring has been introduced which calculates the surface velocity of the glacier by maximizing the Mutual Informantion (MI) between temporal images, yielding the best fit through different displacement (shift) vectors [1,2]. The MI simply measures the statistical dependence between temporal polarimetric covariance matrices by considering the correlation between the polarimetric channels inside the covariance matrices.…”