Unverified Commit 36b8b1d1 authored by Rémi Cresson's avatar Rémi Cresson Committed by GitHub
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Merge pull request #1 from inglada/doc-fixes

Correct errors in file names for the "Practice" section
parents d5ecfb3d 289ed85f
...@@ -313,16 +313,16 @@ Here we will try to provide a simple example of doing a classification using a d ...@@ -313,16 +313,16 @@ Here we will try to provide a simple example of doing a classification using a d
Our data set consists in one Spot-7 image, *spot7.tif*, and a training vector data, *terrain_truth.shp* that qualifies two classes that are forest / non-forest. Our data set consists in one Spot-7 image, *spot7.tif*, and a training vector data, *terrain_truth.shp* that qualifies two classes that are forest / non-forest.
First, we **compute statistics** of the vector data : how many points can we sample inside objects, and how many objects in each class. First, we **compute statistics** of the vector data : how many points can we sample inside objects, and how many objects in each class.
``` ```
otbcli_PolygonClassStatistics -vec terrain_truth.shp -in spot7.tif -out vec_stats.xml otbcli_PolygonClassStatistics -vec terrain_truth.shp -field class -in spot7.tif -out vec_stats.xml
``` ```
Then, we will select some samples with the **SampleSelection** application of the existing machine learning framework of OTB. Then, we will select some samples with the **SampleSelection** application of the existing machine learning framework of OTB.
``` ```
otbcli_SampleSelection -in spot7.tif -vec terrain_truth.shp -instats stats.xml -field class -out points.shp otbcli_SampleSelection -in spot7.tif -vec terrain_truth.shp -instats vec_stats.xml -field class -out points.shp
``` ```
Ok. Now, let's use our **PatchesExtraction** application. Out model has a perceptive field of 16x16 pixels. Ok. Now, let's use our **PatchesExtraction** application. Out model has a perceptive field of 16x16 pixels.
We want to produce one image of patches, and one image for the corresponding labels. We want to produce one image of patches, and one image for the corresponding labels.
``` ```
otbcli_PatchesExtraction -in spot7.tif -patchsizex 16 -patchsizey 16 -vec samplespos.shp -field class -outlabels samp_labels.tif -outpatches samp_patches.tif otbcli_PatchesExtraction -source1.il spot7.tif -source1.patchsizex 16 -source1.patchsizey 16 -vec points.shp -field class -source1.out samp_labels.tif -outpatches samp_patches.tif
``` ```
That's it. Now we have two images for patches and labels. If we wanna, we can split them to distinguish test/validation groups (with the **ExtractROI** application for instance). But here, we will just perform some fine tuning of our model, located in the `outmodel` directory. Our model is quite basic. It has two input placeholders, **x1** and **y1** respectively for input patches (with size 16x16) and input reference labels (with size 1x1). We named **prediction** the tensor that predict the labels and the optimizer that perform the stochastic gradient descent is an operator named **optimizer**. We perform the fine tuning and we export the new model variables in the `newvars` folder. That's it. Now we have two images for patches and labels. If we wanna, we can split them to distinguish test/validation groups (with the **ExtractROI** application for instance). But here, we will just perform some fine tuning of our model, located in the `outmodel` directory. Our model is quite basic. It has two input placeholders, **x1** and **y1** respectively for input patches (with size 16x16) and input reference labels (with size 1x1). We named **prediction** the tensor that predict the labels and the optimizer that perform the stochastic gradient descent is an operator named **optimizer**. We perform the fine tuning and we export the new model variables in the `newvars` folder.
Let's use our **TensorflowModelTrain** application to perform the training of this existing model. Let's use our **TensorflowModelTrain** application to perform the training of this existing model.
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