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Smoothing image by regression in IDL [message #88499] Sat, 03 May 2014 02:39 Go to next message
Dry in water is currently offline  Dry in water
Messages: 18
Registered: October 2012
Junior Member
Hello all,
I'd like to predict NDVI as a function of time using polynomial regression for each land cover separately and integrate them into one image. In other word, I'm going to do temporal smoothing on NDVI image.
I have 2 images; one is time series 8-day NDVI image (26 bands) from Apr to Oct. Another one is Land cover image with 6 classes coded form 1-5. The reason to separate each land cover is just to reduce file size. NDVI image is very big, more than 12GB. Due to big size of image, it is taking very long time for this calculation. Then someone suggested me to use timely average data or spatial subset. Since timely information is my main point, I decided to calculate the regression for each land cover. Anyone to help me?

Thanks in advance
Re: Smoothing image by regression in IDL [message #88501 is a reply to message #88499] Sun, 04 May 2014 09:19 Go to previous messageGo to next message
Dry in water is currently offline  Dry in water
Messages: 18
Registered: October 2012
Junior Member
On Saturday, May 3, 2014 5:39:36 PM UTC+8, Dry in water wrote:
> Hello all,
>
> I'd like to predict NDVI as a function of time using polynomial regression for each land cover separately and integrate them into one image. In other word, I'm going to do temporal smoothing on NDVI image.
>
> I have 2 images; one is time series 8-day NDVI image (26 bands) from Apr to Oct. Another one is Land cover image with 6 classes coded form 1-5. The reason to separate each land cover is just to reduce file size. NDVI image is very big, more than 12GB. Due to big size of image, it is taking very long time for this calculation. Then someone suggested me to use timely average data or spatial subset. Since timely information is my main point, I decided to calculate the regression for each land cover. Anyone to help me?
>
>
>
> Thanks in advance
Re: Smoothing image by regression in IDL [message #88508 is a reply to message #88501] Sun, 04 May 2014 14:38 Go to previous messageGo to next message
Craig Markwardt is currently offline  Craig Markwardt
Messages: 1869
Registered: November 1996
Senior Member
On Sunday, May 4, 2014 12:19:59 PM UTC-4, Dry in water wrote:
> On Saturday, May 3, 2014 5:39:36 PM UTC+8, Dry in water wrote:
>
>> Hello all,
>
>>
>
>> I'd like to predict NDVI as a function of time using polynomial regression for each land cover separately and integrate them into one image. In other word, I'm going to do temporal smoothing on NDVI image.
>
>>
>
>> I have 2 images; one is time series 8-day NDVI image (26 bands) from Apr to Oct. Another one is Land cover image with 6 classes coded form 1-5. The reason to separate each land cover is just to reduce file size. NDVI image is very big, more than 12GB. Due to big size of image, it is taking very long time for this calculation. Then someone suggested me to use timely average data or spatial subset. Since timely information is my main point, I decided to calculate the regression for each land cover. Anyone to help me?

Your problem is so specific that you're unlikely to get a quick and meaningful answer from someone else on the group. Consider: if you don't know what you need, then how can anybody else on the newsgroup know better what you need?

I can only recommend generic advice to make prudent decisions. Consult with colleagues and previously published literature. Try to separate what you want to do - model "something" based on land use type, I guess - from how you will accomplish it - some kind of code written in IDL. This newsgroup is most likely to be able to help on specific questions about IDL.

Best wishes,
CM
Re: Smoothing image by regression in IDL [message #88509 is a reply to message #88499] Sun, 04 May 2014 15:12 Go to previous message
Klemen is currently offline  Klemen
Messages: 80
Registered: July 2009
Member
As Craig wrote, you should be more specific. But from what I understood, I suggest you should go find an appropriate spatial subset where you can train your regression.
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