Non-Gaussian clustering of SAR images for glacier change detection

Our aim is to use unsupervised, non-Gaussian cluster-ing of Arctic glaciers for post-classification change de-tection. Firstly, we demonstrate the consistency of non-Gaussian clustering algorithms for Envisat ASAR im-ages by characterizing the expected random error level for different SAR acquisitio...

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Bibliographic Details
Main Authors: Vahid Akbari, Anthony Doulgeris, Torbjørn Eltoft
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Subjects:
Non-Gaussian
Statistical modeling
Arctic
Asar
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1025.6812
http://munin.uit.no/bitstream/handle/10037/3092/article.pdf%3Bjsessionid%3D88B41996396680CEEA152500EA279589?sequence%3D5
Description
Summary:Our aim is to use unsupervised, non-Gaussian cluster-ing of Arctic glaciers for post-classification change de-tection. Firstly, we demonstrate the consistency of non-Gaussian clustering algorithms for Envisat ASAR im-ages by characterizing the expected random error level for different SAR acquisition conditions (such as inci-dence angle).This allows us to determine whether an ob-served variation is statistically significant and therefore can be used for post-classification change detection of Arctic glaciers. Real significant change was not detected with mixed configurations during the time period of this study.

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