Jose Luis Gomez Dans wrote:
Hi!
I have a series of satellite images of the same region
over a time series. In some cases, there are gaps due
to cloud cover, which are interpolated linearly with
time (i.e., the missing value is interpolated based on
the previous and next values for that location). In
other words, the interpolation is purely made in time,
which sort of works for small temporal spans, but I
would like to have a more robust approach, whereby the
geospatial properties of the images (as well as the
temporal) are also taken into account. Can anyone
recommend any "summer reading" on this?
Thanks
Jose
Here are two examples of using region growing to treat geospatial
properties that are changing over time.
http://www.ldeo.columbia.edu/res/pi/4d4/4d-software.html
Our 4D Seismic Technology: A Case Study in Eugene Island Block 330
covered by 5,586,082 Method for identifying subsurface fluid migration
and drainage pathways in and among oil and gas reservoirs using 3-D and
4-D seismic imaging and my spatial statistics analysis of this method
here http://www.ldeo.columbia.edu/res/pi/4d4/talks/noise/index.html
http://www-ee.eng.hawaii.edu/%7Etreed/ispg/3D_seg_demo/demo.html
3D Segmentation-based Video Compression see also the related
http://www-ee.eng.hawaii.edu/%7Etreed/ispg/ and references there
Image Sequence Processing Group and
http://www-ee.eng.hawaii.edu/%7Etreed/ispg/3D_Gabor_demo/demo.html
Image Sequence Representation Using the 3-D Gabor Transform
Regards,
Albert Boulanger
4D Group
Lamnot-Doherty Earth Observatory
[EMAIL PROTECTED]
* By using the ai-geostats mailing list you agree to follow its rules
( see http://www.ai-geostats.org/help_ai-geostats.htm )
* To unsubscribe to ai-geostats, send the following in the subject or in the
body (plain text format) of an email message to [EMAIL PROTECTED]
Signoff ai-geostats
