diff --git a/doc/EXAMPLES.md b/doc/EXAMPLES.md index 4a9317c2cba0513334101e7bd7be5d9dba11da62..c1d0c2d6e275181e1b03b22257292491b9c248e1 100644 --- a/doc/EXAMPLES.md +++ b/doc/EXAMPLES.md @@ -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 ```