nmileva / starfm4py Goto Github PK

Hi, Nmileva. When I am running code, some windowsize values can yield results, but many other values will report errors after running, indicating that the array shape is different. What should I do?

Daisy

When I run the code, a error like that occurs：cannot import name 'sizeSlices' from 'parameters' (D:\Anaconda3\envs\myenv\lib\site-packages\parameters_init_.py) what should I do next?

I would be very grateful if you could save some time to answer my question.Thanks a lot.

Hi, thank you so much for your great work! I'm trying to use your script to fuse Landsat and MODIS land surface temperature. Have you done any experiment on that? Do you have any suggestions on the parameter settings for this task? Thanks!

Thanks for your valuable work. There was an issue I could not understand while applying the test.py code. How can I apply the code to multi-band images? As I understand it, in order to apply the algorithm to an image containing 6 bands, it is necessary to change the rasterio.open(....read(1)) section in the test.py and repeat this process 6 times (for earch band). Is this approach correct?

Hello,
I have fused Landsat and MODIS with this code. Actually it works, thank you very much for your share.
But the problem I faced is the predicted landsat-like image is a single-band image.
So I tried to change the code in test.py and starfm4py.py.
For example I changed the read(1) to read(), and change ‘’the profile.update(dtype='float64', count=1) to ‘’the profile.update(dtype='float64', count=2) ‘’. However, the ValueError ‘’Chunks and shape must be of the same length/dimension‘’ is generated.
So what I want to know is how to modify the code in a right way.
Best regards,
Xin

Firstly, thank you for your contribution, your code is of great help to me. If it won't bother you too much, I have some little problem to ask you for.
1' How do you evaluate the method. I calculated the PSNR between the result and the GT(groundtruth ), but it's only -44.17. In what way should I do it correctly.
2' The demo 'test' seems to only deal with images with only one spectral band. If I want to use it on hyperspectral images, what should I do? Should I just do it band by band through a 'for' iteration?

I would be very grateful if you could save some time to answer my question

Hi,
Very helpful. I have tried the code to predict Landsat resolution image from Modis data. I am basically trying to reproduce LST at Landsat resolution from Modis data.
However I am facing some issues with the predicted image. Although the LST (surface temperature) range in the predicted image is very close to the actual Landsat data; however the predicted image appears shifted from the input image. All the images had been co-registered, resampled, cloud masked before feeding in the model. Have you come across this issue and suggest a resolution if I have missed any critical step?

Hi Mileva,

Thanks the code was very useful and helpful in understanding the implementation of STARFM. I am working on fusing the Thermal bands of Modis and Landsat data to produce daily Land Surface Temperature (LST) at Landsat Resolution .
While STARFM is useful to start off, I would like to understand if you have also implemented ESTARFM in python and could guide is on that.

Thanks ,
Gunjan

Hi,

This code can be used for two pairs of MODIS and Landsat images?

When I run the code, a error like that occurs：cannot import name 'sizeSlices' from 'parameters' (D:\Anaconda3\envs\myenv\lib\site-packages\parameters_init_.py) what should I do next? Thanks a lot.

Hello, hope you're doing fine.
I cloned the code to test the data you've provided, and installed all the dependencies ( Used Pipenv instead of conda and installed zarr, dask, rasterio and matplotlib successfully).
So just ran the code with default parameters and settings and it seems the code passed the partitioning. but in the da_stack() function encountered error as

ValueError: Need array(s) to concatenate

which came from python3.8/site-packages/dask/array/core.py.
I would appreciate any help.
Thanks by the way.