Commit f1c20894 authored by Cresson Remi's avatar Cresson Remi
Browse files

DOC: add Gaetano et al. reference for two-branch CNN net

parent 4984bd44
......@@ -245,7 +245,7 @@ otbcli_TensorflowModelServe \
It's common that very high resolution products are composed with a panchromatic channel at high-resolution (Pan), and a multispectral image generally at lower resolution (MS).
This model inputs separately the two sources (Pan and MS) separately.
Gaetano, R., Ienco, D., Ose, K., & Cresson, R. (2018). A two-branch CNN architecture for land cover classification of PAN and MS imagery. Remote Sensing, 10(11), 1746.
See: Gaetano, R., Ienco, D., Ose, K., & Cresson, R. (2018). A two-branch CNN architecture for land cover classification of PAN and MS imagery. Remote Sensing, 10(11), 1746.
<img src ="../doc/images/savedmodel_simple_pxs_fcn.png" />
......@@ -290,13 +290,14 @@ otbcli_TensorflowModelServe \
Here we perform the land cover map at the same resolution as the Pan image.
Do do this, we set the Pan image as the first source in the **TensorflowModelServe** application.
Note that this model can not be applied in a fully convolutional fashion at the Pan image resolution.
We hence perform the processing in patch-based mode.
```
otbcli_TensorflowModelServe \
-source1.il $pan -source1.rfieldx 32 -source1.rfieldy 32 -source1.placeholder "x2" \
-source2.il $ms -source2.rfieldx 8 -source2.rfieldy 8 -source2.placeholder "x1" \
-model.dir $modeldir \
-model.fullyconv on \
-output.names "prediction" \
-out $output_classif
```
......@@ -309,7 +310,5 @@ otbcli_TensorflowModelServe \
-source2.il $pan -source2.rfieldx 32 -source2.rfieldy 32 -source2.placeholder "x2" \
-model.dir $modeldir \
-model.fullyconv on \
-output.names "prediction" \
-output.spcscale 0.25 \
-out $output_classif
```
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