SAR change detection techniques and applications
other
ABSTRACT: Change detection, the comparison of remote sensing images from different moments
in time, is an important technique in environmental earth observation and security. SAR change
detection is useful when weather and light conditions are unfavourable. Five methods of SAR
change detection are presented and evaluated. The performance of the first one, post-classification
change detection, is strongly dependent on the accuracy of classification. Because the latter can be
a problem in SAR images, this is not a proper method. The second one is CFAR detection. This
method is only suitable when the changes are small compared to the resolution. The third method
uses an adaptive filter and is able to deal with distributed changes as well. Besides, it can be more
effective in case of small changes, but it lacks in reproducing the shape of changes. The fourth
method makes use of multi-channel segmentation, a method that reproduces the shape of changes
well for multi-look images, but lacks in the detection of small changes. The fifth method is a
combination of CFAR detection, adaptive filtering and multi-channel segmentation. This method
combines the appropriate detection of distributed and small changes, even for noisy SAR images.
As a result there is a preference for the last three methods, but what method can best be used is
dependent on the application too.
in time, is an important technique in environmental earth observation and security. SAR change
detection is useful when weather and light conditions are unfavourable. Five methods of SAR
change detection are presented and evaluated. The performance of the first one, post-classification
change detection, is strongly dependent on the accuracy of classification. Because the latter can be
a problem in SAR images, this is not a proper method. The second one is CFAR detection. This
method is only suitable when the changes are small compared to the resolution. The third method
uses an adaptive filter and is able to deal with distributed changes as well. Besides, it can be more
effective in case of small changes, but it lacks in reproducing the shape of changes. The fourth
method makes use of multi-channel segmentation, a method that reproduces the shape of changes
well for multi-look images, but lacks in the detection of small changes. The fifth method is a
combination of CFAR detection, adaptive filtering and multi-channel segmentation. This method
combines the appropriate detection of distributed and small changes, even for noisy SAR images.
As a result there is a preference for the last three methods, but what method can best be used is
dependent on the application too.
TNO Identifier
221654
Publisher
EARSeL
Source title
Global Developments in Environmental Earth Observation from Space - Proceedings of the 25th Symposium of the European Association of Remote Sensing Laboratories - EARSEL 2005, 6-11 June 2005, Porto, Portugal
Place of publication
Munster
Pages
63-69
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