.. _applications:
Applications
************
Agricultural Applications
=========================
Please visit `LMU Vegetation Apps Documentation `_ for more information.
Classification Dataset Manager
==============================
The :guilabel:`Classification Dataset Manager` allows to
a) create a new dataset from various sources,
b) presents basic information for each category like value, name, color and number of samples,
c) supports editing of category names and colors, and
d) let's you easily draw a random sample.
Usage
1. Start the tool from the :guilabel:`Applications > Classification Dataset Manager` menu.
2. Use the tool for different cases like:
- create a dataset
- edit a dataset
- draw a random subsample
3. Inspect created datasets inside the :guilabel:`Data Sources` panel.
GUI
.. figure:: ./img/ClassificationDatasetManager_1a.png
:align: center
:width: 100%
|
.. figure:: ./img/ClassificationDatasetManager_1b.png
:align: center
:width: 100%
Live demonstration
.. youtube:: qRUrVb6jWbA
:width: 100%
:privacy_mode:
Create a classification dataset
-------------------------------
Select one of the dataset creation options and follow the subsequent algorithm dialog.
GUI
.. figure:: ./img/ClassificationDatasetManager.png
:align: center
:width: 100%
Example data
Datasets used below are available in of the following locations:
- `enmap-box/enmapbox/exampledata/ `_
- `enmap-box/tests/testdata/ `_
From categorized vector layer and feature raster
.. list-table::
:align: center
* - .. figure:: /general/img/raster_layer.png
:height: 400
enmap_potsdam.bsq
- .. figure:: /general/img/categorized_vector_layer_2.png
:height: 400
landcover_potsdam_point.gpkg
.. figure:: ./img/ClassificationDatasetManager_2a.png
:align: center
:width: 100%
For details see the :ref:`algorithm description `.
From categorized raster layer and feature raster
.. list-table::
:align: center
* - .. figure:: /general/img/raster_layer.png
:height: 400
enmap_potsdam.bsq
- .. figure:: /general/img/categorized_raster_layer_2.png
:height: 400
landcover_polygon_30m.tif
.. figure:: ./img/ClassificationDatasetManager_2b.png
:align: center
:width: 100%
For details see the :ref:`algorithm description `.
From categorized spectral library
.. list-table::
:align: center
* - .. figure:: /general/img/categorized_spectral_library.png
library_potsdam.gpkg
.. figure:: ./img/ClassificationDatasetManager_2c.png
:align: center
:width: 100%
For details see the :ref:`algorithm description `.
From categorized vector layer with attribute table
.. figure:: ./img/ClassificationDatasetManager_2d_dataset_2.png
:height: 400
:align: center
classification_dataset.gpkg
.. figure:: ./img/ClassificationDatasetManager_2d_dataset.png
:align: center
:width: 100%
Attribute table with fields Sample_1, Sample_2, ... Sample_177 used as features.
.. figure:: ./img/ClassificationDatasetManager_2d.png
:align: center
:width: 100%
For details see the :ref:`algorithm description `.
From table with categories and feature fields
.. figure:: ./img/ClassificationDatasetManager_2e_dataset.png
:align: center
:width: 100%
Attribute table with fields Band_1, Band_2, ... Band_177 used as features.
.. figure:: ./img/ClassificationDatasetManager_2e.png
:align: center
:width: 100%
For details see the :ref:`algorithm description `.
From Python code
.. figure:: ./img/ClassificationDatasetManager_2f.png
:align: center
:width: 100%
For details see the :ref:`algorithm description `.
From text files
.. figure:: ./img/ClassificationDatasetManager_2g.png
:align: center
:width: 100%
For details see the :ref:`algorithm description `.
From JSON file
.. figure:: ./img/ClassificationDatasetManager_2h.png
:align: center
:width: 100%
For details see the :ref:`algorithm description `.
Edit categories and features
----------------------------
Usage
1. Select a classification dataset.
2. Edit category names and colors inside the :guilabel:`Categories` tab.
3. Edit feature names inside the :guilabel:`Features` tab.
4. :guilabel:`Save` the edits.
Split dataset randomly
----------------------
Usage
1. Select a classification dataset.
2. Set the sample size for each category to be drawn inside the :guilabel:`Categories` tab.
Alternatively, :guilabel:`Set` a relative or absolute sample size used for all categories.
3. Click :guilabel:`Random Sample` and follow the subsequent algorithm dialog.
.. _classification_workflow_app:
Classification workflow
=======================
The :guilabel:`Classification workflow` algorithm let's you easily perform classification analysis and mapping tasks
using remote sensing data.
Usage
1. Start the algorithm from the :guilabel:`Applications > Classification workflow` menu.
2. Select a :guilabel:`Training dataset`.
3. Select a :guilabel:`Classifier`.
4. Select a :guilabel:`Raster layer with features` used for mapping.
5. If cross-validation accuracy assessment is desired,
select the :guilabel:`Number of cross-validation folds` and a
:guilabel:`Output classifier performance report` file destination
(this step is skipped by default).
6. If the classifier supports class probability, you may select an
:guilabel:`Output class probability layer` file destination
(this step is skipped by default).
7. Click :guilabel:`Run`.
GUI
.. figure:: ./img/ClassificationWorkflowAlgorithm.png
:align: center
:width: 100%
Live demonstration
.. youtube:: Tt2XmNuLf5Y
:width: 100%
:privacy_mode:
Classification Workflow (advanced)
==================================
The :guilabel:`Classification Workflow` application let's you easily perform classification analysis and mapping tasks using
remote sensing data.
Quick Mapping
-------------
In the **Quick Mapping** section you can very easily define your training dataset, fit a classifier and predict a
classification layer, with only a few clicks.
Live demonstration
.. youtube:: oi7GeQCik3M
:width: 100%
:privacy_mode:
For a more elaborated analysis see the **Detailed Analysis** section.
Detailed Analysis
-----------------
In the **Detailed Analysis** section you have more control over individual analysis steps.
When performing a detailed analysis, you can basically go through every subsection from left to right.
But, depending on the usecase, it is also possible to skip individual steps you're not interested in.
Live demonstration
.. youtube:: o5rIYXA80VA
:width: 100%
:privacy_mode:
Dataset
.......
You have various options to create a dataset for subsequent analysis: select a :guilabel:`Source` option
and click :guilabel:`create dataset` to create a new dataset`.
In the :guilabel:`Editor`, category colors and names, and feature names can be changed and saved.
By using the various controls in the :guilabel:`Draw samples` section, you can easily define a training-test-split setup.
The number of training and test samples to be drawn for each category are listed, and also editable, inside the :guilabel:`Editor`.
Click :guilabel:`split dataset` to perform the split, resulting in a training and a test dataset, that can be used in subsequent analysis.
Classifier
..........
In the **Classifier** section you can either select a :guilabel:`Predifined` classifier or provide a user-defined Python
:guilabel:`Code` snipped. See the https://scikit-learn.org/ documentation for a complete overview.
Click :guilabel:`create classifier` to create an (unfitted) classifier, that can be used in subsequent analysis.
Feature Clustering
..................
In the **Feature Clustering** section you can perform an unsupervised :guilabel:`Feature redundancy analysis`,
that clusters similar features together: select a :guilabel:`Dataset`, an :guilabel:`Algorithm`
and click :guilabel:`cluster features` to create and an :guilabel:`Output report`.
After inspecting the report you can perform a :guilabel:`Feature subset selection`:
select a suitable :guilabel:`Number of features` and click :guilabel:`select features` to create a training and a test dataset
with fewer features, that are less correlated and can be used in subsequent analysis.
Feature Ranking
...............
In the **Feature Ranking** section you can perform a supervised :guilabel:`Feature importance analysis`,
that ranks features in terms of their importance for the classification task at hand:
select a :guilabel:`Dataset`, an :guilabel:`Algorithm
and click :guilabel:`rank features` to create and an :guilabel:`Output report`.
After inspecting the report you can perform a :guilabel:`Feature subset selection`:
select a suitable :guilabel:`Number of features` and click :guilabel:`select features` to create a training and a test dataset
with fewer features, that are most important and can be used in subsequent analysis.
Model
.....
In the **Model** section you can perform :guilabel:`Model fitting`:
select a :guilabel:`Dataset` and click :guilabel:`fit classifier` to create a fitted :guilabel:`Output classifier`,
that is used in subsequent analysis.
For :guilabel:`Model performance analysis` select an :guilabel:`Algorithm` and click :guilabel:`assess performance` to create an :guilabel:`Output report`.
Classification
..............
In the **Classification** section you can perform :guilabel:`Map prediction`:
select a :guilabel:`Raster layer with features` that matches the features used in :guilabel:`Model fitting`.
Click :guilabel:`predict output products` to create an :guilabel:`Output classification layer` and/or an :guilabel:`Output class probability layer`.
Note that outputs are opened inside the EnMAP-Box :guilabel:`Data Sources` panel.
For :guilabel:`Map accuracy and area estimation` select a :guilabel:`Ground truth categorized layer` and click :guilabel:`assess performance` to create an :guilabel:`Output report`.
Settings
--------
In the **Settings** section you can specify the :guilabel:`Output directory` (e.g. `C:/Users/USERNAME/AppData/Local/Temp/EnMAPBox/ClassificationWorkflow`),
that is used as the default file destination path, when creating file outputs.
Note that each output file wigdet (e.g. :guilabel:`Output dataset`) has a default basename (e.g. `dataset.pkl`),
that is used to create a default file destination (e.g. `C:/Users/USERNAME/AppData/Local/Temp/EnMAPBox/ClassificationWorkflow/dataset.pkl`).
If the default file destination already exists, the basename is enumerated (e.g. `.dataset_2.pkl`) to avoid overwriting existing outputs.
Log
---
Classification Workflow (deprecated)
====================================
Deprecated, use `Classification workflow`_ or `Classification Workflow (advanced)`_ instead.
You can find this application in the menu bar :menuselection:`Applications --> Classification Workflow Classic`
.. figure:: /img/classification_workflow.png
Classification Workflow Application
.. seealso:: Have a look at the :ref:`Getting Started ` for a use case example of the Classification Workflow Application.
Input Parameters:
* **Training Inputs**
* :guilabel:`Type` |combo|
Three different types of input data sources are supported and have to be specified beforehand in the dropdown menu.
Depending on the selected input type the user interface shows different options.
* ``Raster / Classification``:
* :guilabel:`Raster`: Specify input raster based on which samples will be drawn for training a classifier.
* :guilabel:`Classification`: Specify input raster which holds class information.
* ``Raster / Vector Classification``:
* :guilabel:`Raster`: Specify input raster based on which samples will be drawn for training a classifier.
* :guilabel:`Reference`: Specify vector dataset with reference information. Has to have a column in the attribute table with a
unique class identifier (numeric). The class colors and labels are derived from the current Symbology. To set or
change those settings, click the |rendererCategorizedSymbol| button or go to the Layer Properties (:menuselection:`Layer properties --> Symbology`).
The vector dataset is rasterized/burned on-the-fly onto the grid of the input raster in order to extract the sample.
If the vector source is a polygon dataset, only polygons which cover more than 75% of a pixel in the target grid are rasterized.
* ``labelled Library``:
* :guilabel:`Library`: Specify input spectral library.
* **Sampling**
Once you specified all inputs in the Training inputs section, you can edit the class colors, names and class sample sizes
in the Sampling submenu.
.. note::
If set, the class labels and color information is automatically retrieved from the layers current renderer settings
(:menuselection:`Layer properties --> Symbology`).
* :guilabel:`Sample size` |combo| |spin| Specify the sample size per class, either relative in percent or in absolute pixel counts.
* The total sample size is shown below
* |cb0| :guilabel:`Save sample`: Activate this option and specify an output path to save the sample as a raster.
* **Training**
* In the :guilabel:`Classifier` |combo| dropdown menu you can choose different classifiers (e.g. Random Forest, Support Vector Machine)
* |mIconCollapse| :guilabel:`Model parameters`: Specify the parameters of the selected classifier.
.. hint::
Scikit-learn python syntax is used here, which means you can specify model parameters accordingly. Have a look at
the scikit-learn documentation on the individual parameters, e.g. for the `RandomForestClassifier `_
* |cb0| :guilabel:`Save model`: Activate this option to save the model file (:file:`.pkl`) to disk.
* **Mapping**
* :guilabel:`Raster`: Specify the raster you would like to apply the trained classifier to (usually -but not necessarily-
this is the same as used for training)
* :guilabel:`Mask`: Specify a :term:`mask layer` if you want to exclude certain areas from the prediction.
* Outputs:
* :guilabel:`Classification`: Output path where to write the classification image to.
* :guilabel:`Probability`: Output path of the class probability image.
.. hint:: This outputs the result of a classifiers ``predict_proba`` method. Note that depending on the classifier this
option might not be available or has to be activated in the model parameters (e.g. for the Support Vector Machine,
the line ``svc = SVC(probability=False)`` has to be altered to ``svc = SVC(probability=True)``
* :guilabel:`RGB`: Generates a RGB visualisation based on the weighted sum of class colors and class probabilities.
* **Cross-validation Accuracy Assessment**
* |cb0| Cross-validation with n-folds |spin|: Activate this setting to assess the accuracy of the classification by performing cross
validation. Specify the desired number of folds (default: 3). HTML report will be generated at the specified output path.
.. admonition:: Run the classification workflow
Once all parameters are entered, press the |action| button to start the classification workflow.
EO Time Series Viewer
=====================
Please visit `EO Time Series Viewer Documentation `_ for more information.
EnPT (EnMAP Processing Tool)
============================
Please visit `EnPT Tutorial `_ for more information.
GFZ EnGeoMAP
============
Please visit `EnGeoMAP Tutorial `_ for more information.
Image Math (deprecated)
=======================
Deprecated, use `Raster math`_
.. _raster_math_app:
Raster math
===========
The :guilabel:`Raster math` algorithm is a powerful raster calculator inspired by the :guilabel:`QGIS Raster calculator`,
the :guilabel:`GDAL Raster calculator` and :guilabel:`ENVI Band Math`.
In addition to those tools, the EnMAP-Box :guilabel:`Raster math` supports multi-band arrays, vector layer inputs,
multi-line code fragments and metadata handling.
Usage
1. Start the algorithm from the :guilabel:`Applications > Raster math` menu
or from the :guilabel:`Processing Toolbox` panel.
2. Specify a single-line expression or a multi-line code fragment to be evaluated inside the :guilabel:`Code` editor.
Therefore, select raster bands or numeric vector fields from the :guilabel:`Available data sources` tab.
3. [Optional] Select the destination :guilabel:`Grid`. If not specified, the grid of the first raster layer is used.
Note that a) all input raster bands are resampled and b) all input vector fields are rasterized
into the destination grid before the calculation.
4. [Optional] In case you want to perform a spatial operation, be sure to select a proper :guilabel:`Block overlap`
or select :guilabel:`Monolithic processing`, to avoid artefacts at the block edges.
5. [Optional] Note that all inputs are converted to :guilabel:`32-bit floating-point` values by default.
6. [Optional] You can select up to 10 additional raster inputs R1, ..., R10 and vector inputs V1, ..., V10.
Additionally, a list of raster inputs RS can be selected.
7. Select an :guilabel:`Output raster layer` file destination an click :guilabel:`Run`.
GUI
.. figure:: ./img/RasterMath.png
:align: center
:width: 100%
Single-line expressions
-----------------------
Use single-line expressions to evaluate simple numeric formulars.
Example - sum up 3 raster bands using the '+' operator
A raster band is represented as a 2d numpy array and can be selected using the `@` syntax.
``aerial_potsdam@1 + aerial_potsdam@2 + aerial_potsdam@3``
.. youtube:: xK0_whBURQs
:width: 100%
:privacy_mode:
Example - sum up all bands of a raster using `numpy.sum `_ function
A raster is represented as a 3d numpy array and can be selected using the `` syntax.
``np.sum(enmap_potsdam, axis=0)``
Use raster bands
----------------
An individual raster band can be accessed using the `@` syntax, e.g. band number 42::
enmap_potsdam@42
In case of a spectral raster, the band nearest to a target wavelength (in nanometers)
can be selected using the `@nm` syntax, e.g. NIR band at 865 nm::
enmap_potsdam@865nm
Note that prominent target wavelength from the Landsat/Sentinel-2 sensors can be selected inside the
:guilabel:`Waveband locator` tab.
.. figure:: ./img/RasterMath_2.png
:align: center
:width: 100%
All raster bands can be accessed at once using the `` syntax, e.g.::
enmap_potsdam
A band subset can be accessed using the `@:` syntax, e.g. band numbers 10 to 19::
enmap_potsdam@10:20 # note that 20 is not included
# Note that you can also create a band subset by indexing the the full band array.
# This has the slight disadvantage, that all bands are read into memory first.
enmap_potsdam[9:19]
Use vector fields
-----------------
Individual vector fields can be accessed using the `@""` syntax, e.g.::
landcover_potsdam_polygon@"level_3_id"
Note that the vector field is automatically rasterized into the destination :guilabel:`Grid`.
Use raster/vector masks
-----------------------
A raster mask, is a predefined boolean array, which evaluates to `False` for every pixel containing the no data value,
nan or inf. All other pixel evaluate to `True`.
Use the ``Mask`` syntax to access the 3d binary mask for all bands,
and the ``Mask@`` syntax for a 2d single band mask.
2d mask array for a single band: ``enmap_potsdamMask@655nm``
.. figure:: /general/img/mask_raster_layer.png
:height: 400
:align: center
A vector mask is a predefined boolean array, which evaluates to `True` for every pixel covered by a geometry.
All other pixel evaluate to `False`.
Use the ```` syntax to access the 2d binary mask.
2d mask array for a vector layer: ``landcover_potsdam_polygon``
.. figure:: /general/img/mask_raster_layer_2.png
:height: 400
:align: center
Example - mask a raster using a polygon-vector
``enmap_potsdam * landcover_potsdam_polygon``
.. figure:: ./img/RasterMath_3.png
:align: center
:width: 100%
Note that the output raster is correctly masked, but we haven't set an appropriate no data value,
nor have we taken care of wavelength information or any other metadata.
To properly do this, we need to use multi-line code fragments.
Blockwise vs. monolithic processing
-----------------------------------
The computation is done block-wise by default to be memory efficient.
The actual block size depends on the system memory.
In rare cases it may be helpful to get some information about the current block, using the special variable ``block``.
- get the current block extent: ``block.extent``::
- get the current block x/y offset: ``block.xOffset, block.yOffset``::
0, 0
- get the current block x/y size: ``block.width, block.height``::
220, 400
If the computation involves a spatial operation, e.g. a spatial convolution filter with a kernel,
be sure to also specify a proper :guilabel:`Block overlap`.
E.g. for a 5x5 kernel, set at least a block overlap of 2 pixel.
In cases where the spatial operation is not locally limitted to a fixed spatial neightbourhood,
e.g. region growing or segmentation, :guilabel:`Monolithic processing` can be activated,
where all data is processed in one big block.
Multi-line code fragments
-------------------------
To enable more complex computations, multiple outputs and metadata handling, we can use multi-line code fragments.
Example - calculate the NDVI index
In this example we first specify ``_nir`` and ``_red`` variables to then calculate the ``_ndvi``,
which we pass to the special ``outputRaster`` identifier,
that is associated with the :guilabel:`Output raster layer`::
nir_ = enmap_potsdam@865nm
red_ = enmap_potsdam@655nm
ndvi_ = (nir_ - red_) / (nir_ + red_)
outputRaster = ndvi_
The underscore postfix ``_`` marks the ``nir_``, ``red_`` and ``ndvi_`` variables as temporary.
Instead of ``nir_`` we can also use ``_nir``, ``tmp_nir`` or ``temp_nir``.
The explicite assignment ``outputRaster = ndvi_`` can be avoided,
by selecting an :guilabel:`Output raster layer` file destination,
where the file basename (without extension) matches the variable name,
e.g. `c:/ndvi.tif`::
nir_ = enmap_potsdam@865nm
red_ = enmap_potsdam@655nm
ndvi = (nir_ - red_) / (nir_ + red_) # ndvi matches with c:/ndvi.tif
If the file basename isn't matching correctly, you will get the following error message inside the Log panel::
The following layers were not correctly generated.
• C:/Users/Andreas/AppData/Local/Temp/processing_BVyjbt/3a6c795d9a594937acf441c5a372f448/outputRaster.tif
You can check the 'Log Messages Panel' in QGIS main window to find more information about the execution of the algorithm.
Instead of using temporary variables, you can also just delete unwanted variables as a last step::
nir = enmap_potsdam@865nm
red = enmap_potsdam@655nm
ndvi = (nir - red) / (nir + red)
del nir, red # delete temporary variables manually
Example - calculate multiple outputs
To calculate multiple outputs, just define multiple non-temporary variables::
N = enmap_potsdam@865nm / 1e4 # EVI needs data scaled to 0-1 range
R = enmap_potsdam@655nm / 1e4
B = enmap_potsdam@482nm / 1e4
ndvi = (N - R) / (N + R)
evi = 2.5 * (N - R) / (N + 6 * R - 7.5 * B + 1)
del N, R, B
Note that you can only specify the file destination of one of the outputs,
e.g. by setting :guilabel:`Output raster layer` to `c:/results/ndvi.tif` or `c:/results/evi.tif`.
The other output is written into the same directory as a GeoTiff with the basename
matching the variable name `c:/results/.tif`.
You may also keep the default file destination `[Save to temporary file]` as is,
to write all outputs into a temp folder. In this case, it is fine to just ignore the error message::
The following layers were not correctly generated.
• C:/Users/Andreas/AppData/Local/Temp/processing_BVyjbt/3a6c795d9a594937acf441c5a372f448/outputRaster.tif
You can check the 'Log Messages Panel' in QGIS main window to find more information about the execution of the algorithm.
Metadata handling
-----------------
You have full access to the underlying raster metadata like:
- band no data value: ``enmap_potsdam.noDataValue(bandNo=1)``::
-99.0
- band name: ``enmap_potsdam.bandName(bandNo=1)``::
band 8 (0.460000 Micrometers)
- band-level metadata dictionary: ``enmap_potsdam.metadata(bandNo=1)``::
{'': {'wavelength': '0.460000', 'wavelength_units': 'Micrometers'}}
- band-level metadata item: ``enmap_potsdam.metadataItem(key='wavelength_units', domain='', bandNo=1)``::
Micrometers
- dataset-level metadata dictionary: ``enmap_potsdam.metadata()``::
{'IMAGE_STRUCTURE': {'INTERLEAVE': 'BAND'}, '': {'wavelength_units': 'Micrometers'}, 'ENVI': {'acquisition_time': '2009-08-20T09:44:50', 'bands': '177', 'band_names': ['band 8', 'band 9', 'band 10', 'band 11', 'band 12', 'band 13', 'band 14', 'band 15', 'band 16', 'band 17', 'band 18', 'band 19', 'band 20', 'band 21', 'band 22', 'band 23', 'band 24', 'band 25', 'band 26', 'band 27', 'band 28', 'band 29', 'band 30', 'band 31', 'band 32', 'band 33', 'band 34', 'band 35', 'band 36', 'band 37', 'band 38', 'band 39', 'band 40', 'band 41', 'band 42', 'band 43', 'band 44', 'band 45', 'band 46', 'band 47', 'band 48', 'band 49', 'band 50', 'band 51', 'band 52', 'band 53', 'band 54', 'band 55', 'band 56', 'band 57', 'band 58', 'band 59', 'band 60', 'band 61', 'band 62', 'band 63', 'band 64', 'band 65', 'band 66', 'band 67', 'band 68', 'band 69', 'band 70', 'band 71', 'band 72', 'band 73', 'band 74', 'band 75', 'band 76', 'band 77', 'band 91', 'band 92', 'band 93', 'band 94', 'band 95', 'band 96', 'band 97', 'band 98', 'band 99', 'band 100', 'band 101', 'band 102', 'band 103', 'band 104', 'band 105', 'band 106', 'band 107', 'band 108', 'band 109', 'band 110', 'band 111', 'band 112', 'band 113', 'band 114', 'band 115', 'band 116', 'band 117', 'band 118', 'band 119', 'band 120', 'band 121', 'band 122', 'band 123', 'band 124', 'band 125', 'band 126', 'band 127', 'band 144', 'band 145', 'band 146', 'band 147', 'band 148', 'band 149', 'band 150', 'band 151', 'band 152', 'band 153', 'band 154', 'band 155', 'band 156', 'band 157', 'band 158', 'band 159', 'band 160', 'band 161', 'band 162', 'band 163', 'band 164', 'band 165', 'band 166', 'band 167', 'band 168', 'band 195', 'band 196', 'band 197', 'band 198', 'band 199', 'band 200', 'band 201', 'band 202', 'band 203', 'band 204', 'band 205', 'band 206', 'band 207', 'band 208', 'band 209', 'band 210', 'band 211', 'band 212', 'band 213', 'band 214', 'band 215', 'band 216', 'band 217', 'band 218', 'band 219', 'band 220', 'band 221', 'band 222', 'band 223', 'band 224', 'band 225', 'band 226', 'band 227', 'band 228', 'band 229', 'band 230', 'band 231', 'band 232', 'band 233', 'band 234', 'band 235', 'band 236', 'band 237', 'band 238', 'band 239'], 'byte_order': '0', 'coordinate_system_string': ['PROJCS["UTM_Zone_33N"', 'GEOGCS["GCS_WGS_1984"', 'DATUM["D_WGS_1984"', 'SPHEROID["WGS_1984"', '6378137.0', '298.257223563]]', 'PRIMEM["Greenwich"', '0.0]', 'UNIT["Degree"', '0.0174532925199433]]', 'PROJECTION["Transverse_Mercator"]', 'PARAMETER["False_Easting"', '500000.0]', 'PARAMETER["False_Northing"', '0.0]', 'PARAMETER["Central_Meridian"', '15.0]', 'PARAMETER["Scale_Factor"', '0.9996]', 'PARAMETER["Latitude_Of_Origin"', '0.0]', 'UNIT["Meter"', '1.0]]'], 'data_ignore_value': '-99', 'data_type': '2', 'description': ['EnMAP02_potsdam_Urban_Gradient_2009.bsq', 'http://doi.org/10.5880/enmap.2016.008', 'spectral and spatial subset'], 'file_type': 'ENVI Standard', 'fwhm': ['0.005800', '0.005800', '0.005800', '0.005800', '0.005800', '0.005800', '0.005800', '0.005800', '0.005800', '0.005800', '0.005900', '0.005900', '0.006000', '0.006000', '0.006100', '0.006100', '0.006200', '0.006200', '0.006300', '0.006400', '0.006400', '0.006500', '0.006600', '0.006600', '0.006700', '0.006800', '0.006900', '0.006900', '0.007000', '0.007100', '0.007200', '0.007300', '0.007300', '0.007400', '0.007500', '0.007600', '0.007700', '0.007800', '0.007900', '0.007900', '0.008000', '0.008100', '0.008200', '0.008300', '0.008400', '0.008400', '0.008500', '0.008600', '0.008700', '0.008700', '0.008800', '0.008900', '0.008900', '0.009000', '0.009100', '0.009100', '0.009200', '0.009300', '0.009300', '0.009400', '0.009400', '0.009500', '0.009500', '0.009600', '0.009600', '0.009600', '0.009600', '0.009700', '0.009700', '0.009700', '0.011800', '0.011900', '0.012100', '0.012200', '0.012400', '0.012500', '0.012700', '0.012800', '0.012900', '0.013100', '0.013200', '0.013300', '0.013400', '0.013500', '0.013600', '0.013700', '0.013800', '0.013900', '0.014000', '0.014000', '0.014100', '0.014100', '0.014200', '0.014200', '0.014300', '0.014300', '0.014300', '0.014400', '0.014400', '0.014400', '0.014400', '0.014400', '0.014400', '0.014400', '0.014400', '0.014400', '0.014400', '0.013700', '0.013600', '0.013600', '0.013500', '0.013500', '0.013400', '0.013400', '0.013300', '0.013200', '0.013200', '0.013100', '0.013100', '0.013000', '0.012900', '0.012900', '0.012800', '0.012800', '0.012700', '0.012700', '0.012600', '0.012500', '0.012500', '0.012400', '0.012400', '0.012300', '0.010900', '0.010800', '0.010800', '0.010700', '0.010700', '0.010600', '0.010600', '0.010500', '0.010500', '0.010400', '0.010400', '0.010400', '0.010300', '0.010300', '0.010200', '0.010200', '0.010100', '0.010100', '0.010100', '0.010000', '0.010000', '0.009900', '0.009900', '0.009900', '0.009800', '0.009800', '0.009700', '0.009700', '0.009700', '0.009600', '0.009600', '0.009600', '0.009500', '0.009500', '0.009400', '0.009400', '0.009400', '0.009300', '0.009300', '0.009300', '0.009200', '0.009200', '0.009100', '0.009100', '0.009100'], 'header_offset': '0', 'interleave': 'bsq', 'lines': '400', 'reflectance_scale_factor': '10000', 'samples': '220', 'sensor_type': 'Unknown', 'wavelength': ['0.460000', '0.465000', '0.470000', '0.475000', '0.479000', '0.484000', '0.489000', '0.494000', '0.499000', '0.503000', '0.508000', '0.513000', '0.518000', '0.523000', '0.528000', '0.533000', '0.538000', '0.543000', '0.549000', '0.554000', '0.559000', '0.565000', '0.570000', '0.575000', '0.581000', '0.587000', '0.592000', '0.598000', '0.604000', '0.610000', '0.616000', '0.622000', '0.628000', '0.634000', '0.640000', '0.646000', '0.653000', '0.659000', '0.665000', '0.672000', '0.679000', '0.685000', '0.692000', '0.699000', '0.706000', '0.713000', '0.720000', '0.727000', '0.734000', '0.741000', '0.749000', '0.756000', '0.763000', '0.771000', '0.778000', '0.786000', '0.793000', '0.801000', '0.809000', '0.817000', '0.824000', '0.832000', '0.840000', '0.848000', '0.856000', '0.864000', '0.872000', '0.880000', '0.888000', '0.896000', '0.915000', '0.924000', '0.934000', '0.944000', '0.955000', '0.965000', '0.975000', '0.986000', '0.997000', '1.007000', '1.018000', '1.029000', '1.040000', '1.051000', '1.063000', '1.074000', '1.086000', '1.097000', '1.109000', '1.120000', '1.132000', '1.144000', '1.155000', '1.167000', '1.179000', '1.191000', '1.203000', '1.215000', '1.227000', '1.239000', '1.251000', '1.263000', '1.275000', '1.287000', '1.299000', '1.311000', '1.323000', '1.522000', '1.534000', '1.545000', '1.557000', '1.568000', '1.579000', '1.590000', '1.601000', '1.612000', '1.624000', '1.634000', '1.645000', '1.656000', '1.667000', '1.678000', '1.689000', '1.699000', '1.710000', '1.721000', '1.731000', '1.742000', '1.752000', '1.763000', '1.773000', '1.783000', '2.044000', '2.053000', '2.062000', '2.071000', '2.080000', '2.089000', '2.098000', '2.107000', '2.115000', '2.124000', '2.133000', '2.141000', '2.150000', '2.159000', '2.167000', '2.176000', '2.184000', '2.193000', '2.201000', '2.210000', '2.218000', '2.226000', '2.234000', '2.243000', '2.251000', '2.259000', '2.267000', '2.275000', '2.283000', '2.292000', '2.300000', '2.308000', '2.315000', '2.323000', '2.331000', '2.339000', '2.347000', '2.355000', '2.363000', '2.370000', '2.378000', '2.386000', '2.393000', '2.401000', '2.409000'], 'wavelength_units': 'Micrometers', 'y_start': '24', 'z_plot_titles': ['wavelength [!7l!3m]!N', 'reflectance [*10000]']}}}
- dataset-level metadata item: ``enmap_potsdam.metadataItem(key='wavelength_units', domain='')``::
Micrometers
In general, all the methods provided by the **RasterReader** class can be used:
https://github.com/EnMAP-Box/enmap-box/blob/main/enmapboxprocessing/rasterreader.py
For the outputs, you can use all the methods provided by the **RasterWriter** class:
https://github.com/EnMAP-Box/enmap-box/blob/main/enmapboxprocessing/rasterwriter.py
Also note the shortcuts inside the :guilabel:`Available data sources` tab context menu
and the :guilabel:`Data / Metadata` tab.
.. figure:: ./img/RasterMath_4.png
:align: center
:width: 100%
Example - calculate the NDVI index and set up metadata properly
This example shows how to properly calculate the NDVI index, masking no data pixel and set up output metadata::
# find bands
red = enmap_potsdam@655nm
nir = enmap_potsdam@865nm
# calculate NDVI
ndvi = (nir - red) / (nir + red)
# mask no data region
noDataValue = -9999
ndvi[~enmap_potsdamMask@655nm] = noDataValue
ndvi[~enmap_potsdamMask@865nm] = noDataValue
# set no data value and band name
ndvi.setNoDataValue(noDataValue)
ndvi.setBandName('NDVI', bandNo=1)
# clean up temp variables
del red, nir
Example - copy raster data and metadata
This example shows how to properly copy a raster data and metadata::
# copy data
copy = enmap_potsdam
# copy metadata
copy.setMetadata(enmap_potsdam.metadata())
for bandNo in enmap_potsdam.bandNumbers():
copy.setMetadata(enmap_potsdam.metadata(bandNo), bandNo)
copy.setBandName(enmap_potsdam.bandName(bandNo), bandNo)
copy.setNoDataValue(enmap_potsdam.noDataValue(bandNo), bandNo)
Input raster lists
------------------
For some operations it may be necessary to enter an arbitrary large list of rasters.
In this case, use the :guilabel:`Raster layers mapped to RS`.
Example - average a list of rasters
``np.mean(RS, axis=0)``
As for normal input raster, use the ``RSMask`` syntax to access the binary no data value masks.
Regression Dataset Manager
==========================
todo
Regression Workflow (deprecated)
================================
.. seealso:: Have a look at the :ref:`Biomass Mapping Tutorial ` for a use case example of the Regression Workflow Application.
Input Parameters:
* **Training Inputs**
* :guilabel:`Raster`: Specify input raster based on which samples will be drawn for training a regressor.
* :guilabel:`Reference`: Specify vector or raster dataset with reference information (regression target). In case of
vector input, dataset has to have a numeric column in the attribute table with a
target variable of interest. This vector dataset is rasterized/burned on-the-fly onto the grid of
the input raster in order to extract the sample. If the vector source is a polygon dataset, all pixels will be drawn which
intersect the polygon.
* :guilabel:`Attribute`: Attribute field in the reference vector layer which contains the regression target variable.
* **Sampling**
* :guilabel:`Number of Strata` |spin|: Specify the desired number of strata sampling. If you don't want to use
stratified sampling, just enter ``1``.
* :guilabel:`Min` & :guilabel:`Max`: Defines the value range in which samples should be drawn.
* :guilabel:`Sample size` |combo| |spin|: Specify the sample size per stratum, either relative in percent or in absolute pixel counts.
Every stratum is listed below with the value range that is covered by this stratum depicted in square brackets
(e.g., ``Stratum 1: [1.0, 4.33]``). Furthermore, you can see and alter the number of pixels/samples for each stratum using the |spin| spinboxes.
* |cb0| :guilabel:`Save sample`: Activate this option and specify an output path to save the sample as a raster.
* **Training**
* In the :guilabel:`Regressor` |combo| dropdown menu you can choose different regressors (e.g. Random Forest, Support Vector Regression, Kernel Ridge Regression)
* |mIconCollapse| :guilabel:`Model parameters`: Specify the parameters of the selected regressor.
.. hint::
Scikit-learn python syntax is used here, which means you can specify model parameters accordingly. Have a look at
the scikit-learn documentation on the individual parameters, e.g. for the `RandomForestRegressor `_
* |cb0| :guilabel:`Save model`: Activate this option to save the model file (:file:`.pkl`) to disk.
* **Mapping**
* :guilabel:`Input Raster`: Specify the raster you would like to apply the trained regressor to (usually -but not necessarily-
this is the same as used for training)
* **Cross-validation Accuracy Assessment**
* |cb0| Cross-validation with n-folds |spin|: Activate this setting to assess the accuracy of the regression by performing cross
validation. Specify the desired number of folds (default: 3). HTML report will be generated at the specified output path.
.. admonition:: Run the regression workflow
Once all parameters are entered, press the |action| button to start the regression workflow.
Spectral Index Creator
==================================
todo
.. AUTOGENERATED SUBSTITUTIONS - DO NOT EDIT PAST THIS LINE
.. |action| image:: /img/icons/action.svg
:width: 28px
.. |cb0| image:: /img/icons/cb0.png
:width: 28px
.. |combo| image:: /img/icons/combo.png
:width: 28px
.. |mIconCollapse| image:: /img/icons/mIconCollapse.svg
:width: 28px
.. |rendererCategorizedSymbol| image:: /img/icons/rendererCategorizedSymbol.svg
:width: 28px
.. |spin| image:: /img/icons/spin.png
:width: 28px