A New Coherent Similarity Measure for Temporal Multichannel Scene Characterization

Erten, Esra; Reigber, Andreas; Ferro-Famil, Laurent; Hellwich, Olaf

doi:10.1109/tgrs.2011.2174155

Cited by 44 publications

(33 citation statements)

References 31 publications

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“…This paper expands the ideas firstly presented in (Erten et al, 2012), which uses the MI to characterize the temporal scene in terms of PolSAR images. In this work, the mutual information, which is maximum if the temporal images are geometrically aligned, is used for multi-channel (PolSAR) tracking by exploiting the second order statistics of the acquisitions.…”

Section: Glacier Surface Monitoringmentioning

confidence: 90%

“…However, in the case of polarimetric images, it is really time consuming work due to the requirement of 6 dimensional joint histogram. To overcome this problem joint distribution between temporal polarimetric covariance matrices derived by Erten et al, 2012 is used.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Taking the joint distribution p(A11, A22) of temporal polarimetric covariance matrices A11 and A22, the MI between them due to the Wishart process in time is (Erten et al, 2012):…”

Section: Proposed Algorithmmentioning

confidence: 99%

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Glacier Surface Monitoring by Maximizing Mutual Information

Erten

Rossi

Hajnsek

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Section: Glacier Surface Monitoringmentioning

confidence: 90%

Section: Proposed Algorithmmentioning

confidence: 99%

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Glacier Surface Monitoring by Maximizing Mutual Information

Erten

Rossi

Hajnsek

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…MI is a nonparametric approach that does not require any assumption about the shape of the distribution of input variables and able to measure both the linear and nonlinear relationships among input variables [19]. A lot of papers considered MI as a similarity measure for change detection task including multivariate statistical model [20] which concentrated on homogeneous and heterogeneous sensors, SAR image change detection [21] which considered different kind of changes, canonical information analysis [22] for image change detection, multicontextual mutual information data as an improved form of image spatial mutual information [23] for SAR image change detection, and temporal behavior of multichannel scene characterization for change detection [24].…”

Section: Introductionmentioning

confidence: 99%

Statistical Similarity Based Change Detection for Multitemporal Remote Sensing Images

Aktar

Mamun

Hossain

2017

Journal of Electrical and Computer Engineering

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Change detection (CD) of any surface using multitemporal remote sensing images is an important research topic since up-to-date information about earth surface is of great value. Abrupt changes are occurring in different earth surfaces due to natural disasters or man-made activities which cause damage to that place. Therefore, it is necessary to observe the changes for taking necessary steps to recover the subsequent damage. This paper is concerned with this issue and analyzes statistical similarity measure to perform CD using remote sensing images of the same scene taken at two different dates. A variation of normalized mutual information (NMI) as a similarity measure has been developed here using sliding window of different sizes. In sliding window approach, pixels' local neighborhood plays a significant role in computing the similarity compared to the whole image. Thus the insignificant global characteristics containing noise and sparse samples can be avoided when evaluating the probability density function. Therefore, NMI with different window sizes is proposed here to identify changes using multitemporal data. Experiments have been carried out using two separate multitemporal remote sensing images captured one year apart and one month apart, respectively. Experimental analysis reveals that the proposed technique can detect up to 97.71% of changes which outperforms the traditional approaches.

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“…• the time warping distance associated with a diskbased sux tree indexing is a accurate in comparing sequences of dierent lengths and/or dierent sampling rates, see [8], • the Bhattacharyya distance between the probability density functions is ecient for measuring contrast similarity, see [9], • the mutual information between Wishart processes over time is suitable for characterizing temporal polarimetric and interferometric informations in SAR data, see [10]. Exploiting new generation remote sensing images is actually facing three major challenges: high resolution, large database (long acquisition sequence) and speckle eect.…”

Section: Introductionmentioning

confidence: 99%