Reproduction of results

[davidiagraid]

Hello,

I started playing with the models you provided for Super-Resolution, first trying to reproduce the results from the article Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural
network about the method.
The approach is the following : I took 4 images of size (110 `km)^2 directly from the testing set shared in the git repository (
London : S2A_MSIL1C_20170522T110621_N0205_R137_T30UXC_20170522T110912
El Salvador : S2A_MSIL1C_20170419T155901_N0204_R097_T16PEU_20170419T161354
Japan City : S2A_MSIL1C_20170216T015741_N0204_R060_T52SFB_20170216T015924
NZ mountains : S2A_MSIL1C_20170616T223701_N0205_R072_T59GLL_20170616T223702 )

For the T_{2x} model, I downsampled the 10 and 20m GDS bands (A and B bands) to respectively 20 and 40m GSD, and then I applied the DSen2_20 function in your code to these (which corresponds to the inference with the trained model) to get the B bands super resoluted. Then, I computed the band wise root mean squared error between the super resoluted (20m GSD) B bands and the Ground truth 20m GSD B bands. I did the analog for the S_{6x} model, as described in the paper.

As a result, I obtained this : (in orange : the RMSE for Bicubic and in blue, the RMSE for DSen2)

.The DSen2 model performs slightly better than the bicubic interpolation (at best 1.1 lower RMSE than Bicubic). My problem is that I cannot get the same results as in the paper (3 to 4 times lower RMSE than Bicubic).
Is there something I missed in the pipeline ?

Also, in the Demo code, you compare the super-resoluted B bands with a ground truth called imGT. Is it a 10m GSD ground truth (in that case, is there a specific way to get this resolution ground truth for the B bands ?), or all the input images (im10 and im20) have been downsampled before ?

[lanha]

Hello,
I suspect the issue comes from:
"I downsampled the 10 and 20m GDS bands (A and B bands) to respectively 20 and 40m GSD".
How did you perform this? Have you tried this function:
https://github.com/lanha/DSen2/blob/master/utils/patches.py#L353

is there a specific way to get this resolution ground truth for the B bands ?),

no

or all the input images (im10 and im20) have been downsampled before ?

yes, you are correct, all have been downsampled before.

[davidiagraid]

#How did you perform this? Have you tried this function:
Yes I used the downPixelAggr function you premade for this.
The main part of my code is :

` data360 = downPixelAggr(data60_gt, SCALE=6)
data60 = downPixelAggr(data10_gt, SCALE=6)
data120 = downPixelAggr(data20_gt, SCALE=6) ##Downsampling 10m,20m,60m to 60m,120m,360m
print("Super-resolving the 360m downsampled data into 60m bands")
sr60 = DSen2_60(data60, data120, data360, deep=False)

print("Super-resolving the 40m downsampled data into 20m bands")
data40 = downPixelAggr(data20_gt, SCALE = 2)
data20 = downPixelAggr(data10_gt, SCALE=2) ##Downsampling 10m,20m to 20m,40m
sr20 = DSen2_20(data20, data40, deep=False)
for i in range(np.shape(sr20)[-1]):  
    rmse20 = RMSE(sr20[:,:,i], data20_gt[:,:,i]) #Computing the RMSE per band between the Ground truth and the super resoluted image from the model

SR_bicubic_20 = imresize(data40,output_shape = np.shape(data20_gt)) #Computing the bicubic upsampling
for i in range(np.shape(sr20)[-1]):
    metrics['Bicubic_20'][data20m[i]] = RMSE(SR_bicubic_20[:,:,i], data20_gt[:,:,i]) #Computing the RMSE between the Bicubic upsampled image and the ground truth
`

#yes, you are correct, all have been downsampled before.
Thank you, it makes more sense now

[lanha]

I don't see anything abnormal in the snippet you shared.