Sampling applications¶
OTBTF sampling applications are OTB applications that focus on the extraction of samples in the remote sensing images.
Main OTBTF applications for sampling are:
Other applications were written for experimental and educational purposes, but could still fill some needs sometimes:
PatchesSelection¶
This application generate points sampled at regular interval over the input image region. The selection strategy, patches grid size and step can be configured. The application produces a vector data containing a set of points centered on the patches after the selection process.
The following strategies are implemented:
- Split: the classic training/validation/testing samples split,
- Chessboard: training/validation over the patches grid in a chessboard fashion
- All: all patches are selected
- Balanced: using an additional terrain truth labels map to select patches a random locations that try to balance the patches population distribution, based on the class value.
The application description can be displayed using:
PatchesExtraction¶
The PatchesExtraction application performs the extraction of patches in
images from the following:
- a vector data containing points (mandatory)
- at least one imagery source (mandatory). To change the number of sources,
set the environment variable
OTB_TF_NSOURCES - One exiting field name of the vector data to identify the different points.
Each point of the vector data locates the center of the central pixel of one patch. For each source, the following parameters can be set:
- the patch size (x and y): for patches with even size N, the central pixel corresponds to the pixel index N/2+1 (index starting at 0).
- a no-data value: If any pixel value inside the patch is equal to the provided value, the patch is rejected.
- an output file name for the patches image that the application exports at the end of the sampling. Patches are stacked in rows and exported as common raster files supported by GDAL, without any geographical information.
Example with 2 sources¶
We denote one input source, either an input image, or a stack of input images
that will be concatenated (they must have the same size).
The user can set the OTB_TF_NSOURCES environment variable to select the
number of input sources that he wants.
For example, for sampling a Time Series (TS) together with a single Very High
Resolution image (VHR), two sources are required:
- 1 input images list for time series,
- 1 input image for the VHR.
The sampled patches are extracted at each position designed by the input vector data, only if a patch lies fully in all input sources extents. For each input source, patches sizes must be provided. For each input source, the application export all sampled patches as a single multiband raster, stacked in rows. For instance, for n samples of size 16 x 16 from a 4 channels input source, the output image will be a raster of size 16 x 16n with 4 channels. An optional output is an image of size 1 x n containing the value of one specific field of the input vector data. Typically, the class field can be used to generate a dataset suitable for a model that performs pixel wise classification.
The application description can be displayed using:
DensePolygonClassStatistics¶
This application is a clone of the PolygonClassStatistics
application from OTB modified to use rasterization instead of vector based
approach, making it faster.
The application description can be displayed using:
LabelImageSampleSelection¶
This application extracts points from an input label image. This application
is like SampleSelection, but uses an input label image, rather than an input
vector data. It produces a vector data containing a set of points centered on
the pixels of the input label image. The user can control the number of
points. The default strategy consists in producing the same number of points
in each class. If one class has a smaller number of points than requested,
this one is adjusted.
The application description can be displayed using:
Example¶
Below is a minimal example that presents some steps to sample patches from a sparse annotated vector data as terrain truth. Let's consider that our data set consists in one Spot-7 image, spot7.tif, and a training vector data, terrain_truth.shp that describes sparsely forest / non-forest polygons.
First, we compute statistics of the vector data : how many points can we sample
inside objects, and how many objects in each class.
We use the PolygonClassStatistics application of OTB.
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.
Since the terrain truth is sparse, we want to sample randomly points in
polygons with the default strategy of the SampleSelection OTB application.
otbcli_SampleSelection -in spot7.tif -vec terrain_truth.shp \
-instats vec_stats.xml -field class -out points.shp
Now we extract the patches with the PatchesExtraction application.
We want to produce one image of 16x16 patches, and one image for the
corresponding labels.
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
Now we can use the generated samp_patches.tif and samp_labels.tif in the
TensorflowModelTrain application, or using the python API to build and train
models with Keras.