Feature extraction
==================
As described in the OTB Software Guide, the term *Feature Extraction*
refers to techniques aiming at extracting added value information from
images. These extracted items named *features* can be local statistical
moments, edges, radiometric indices, morphological and textural
properties. For example, such features can be used as input data for
other image processing methods like *Segmentation* and *Classification*.
Local statistics extraction
---------------------------
This application computes the 4 local statistical moments on every pixel
in the selected channel of the input image, over a specified
neighborhood. The output image is multi band with one statistical moment
(feature) per band. Thus, the 4 output features are the Mean, the
Variance, the Skewness and the Kurtosis. They are provided in this exact
order in the output image.
The *LocalStatisticExtraction* application has the following input
parameters:
-``-in`` the input image to compute the features on
-``-channel`` the selected channel index in the input image to be
processed (default value is 1)
-``-radius`` the computational window radius (default value is 3
pixels)
-``-out`` the output image containing the local statistical moments
The application can be used like this:
::
otbcli_LocalStatisticExtraction -in InputImage
-channel 1
-radius 3
-out OutputImage
Edge extraction
---------------
This application Computes edge features on every pixel in the selected
channel of the input image.
The *EdgeExtraction* application has the following input parameters:
-``-in`` the input image to compute the features on
-``-channel`` the selected channel index in the input image to be
processed (default value is 1)
- ``-filter`` the choice of edge detection method (gradient/sobel/touzi) (default value is gradient) Â Â
-``(-filter.touzi.xradius)`` the X Radius of the Touzi processing neighborhood (only if filter==touzi) (default value is 1 pixel) __
- ``(-filter.touzi.yradius)`` the Y Radius of the Touzi processing neighborhood (only if filter==touzi) (default value is 1 pixel) Â Â
-``-out`` the output mono band image containing the edge features
The application can be used like this:
::
otbcli_EdgeExtraction -in InputImage
-channel 1
-filter sobel
-out OutputImage
or like this if filter==touzi:
::
otbcli_EdgeExtraction -in InputImage
-channel 1
-filter touzi
-filter.touzi.xradius 2
-filter.touzi.yradius 2
-out OutputImage
Radiometric indices extraction
------------------------------
This application computes radiometric indices using the channels of the
input image. The output is a multi band image into which each channel is
one of the selected indices.
The *RadiometricIndices* application has the following input parameters:
-``-in`` the input image to compute the features on
-``-out`` the output image containing the radiometric indices
-``-channels.blue`` the Blue channel index in the input image (default
value is 1)
-``-channels.green`` the Green channel index in the input image
(default value is 1)
-``-channels.red`` the Red channel index in the input image (default
value is 1)
-``-channels.nir`` the Near Infrared channel index in the input image
(default value is 1)
-``-channels.mir`` the Mid-Infrared channel index in the input image
(default value is 1)
-``-list`` the list of available radiometric indices (default value is
Vegetation:NDVI)
The available radiometric indices to be listed into -list with their
relevant channels in brackets are:
::
Vegetation:NDVI - Normalized difference vegetation index (Red, NIR)
Vegetation:TNDVI - Transformed normalized difference vegetation index (Red, NIR)
Vegetation:RVI - Ratio vegetation index (Red, NIR)
Vegetation:SAVI - Soil adjusted vegetation index (Red, NIR)
Vegetation:TSAVI - Transformed soil adjusted vegetation index (Red, NIR)
Vegetation:MSAVI - Modified soil adjusted vegetation index (Red, NIR)
Vegetation:MSAVI2 - Modified soil adjusted vegetation index 2 (Red, NIR)
Vegetation:GEMI - Global environment monitoring index (Red, NIR)
Vegetation:IPVI - Infrared percentage vegetation index (Red, NIR)
Water:NDWI - Normalized difference water index (Gao 1996) (NIR, MIR)
Water:NDWI2 - Normalized difference water index (Mc Feeters 1996) (Green, NIR)
Water:MNDWI - Modified normalized difference water index (Xu 2006) (Green, MIR)
Water:NDPI - Normalized difference pond index (Lacaux et al.) (MIR, Green)
Water:NDTI - Normalized difference turbidity index (Lacaux et al.) (Red, Green)
Soil:RI - Redness index (Red, Green)
Soil:CI - Color index (Red, Green)
Soil:BI - Brightness index (Red, Green)
Soil:BI2 - Brightness index 2 (NIR, Red, Green)
The application can be used like this, which leads to an output image
with 3 bands, respectively with the Vegetation:NDVI, Vegetation:RVI and
Vegetation:IPVI radiometric indices in this exact order:
::
otbcli_RadiometricIndices -in InputImage
-out OutputImage
-channels.red 3
-channels.green 2
-channels.nir 4
-list Vegetation:NDVI Vegetation:RVI
Vegetation:IPVI
or like this, which leads to a single band output image with the
Water:NDWI2 radiometric indice:
::
otbcli_RadiometricIndices -in InputImage
-out OutputImage
-channels.red 3
-channels.green 2
-channels.nir 4
-list Water:NDWI2
Morphological features extraction
---------------------------------
Morphological features can be highlighted by using image filters based
on mathematical morphology either on binary or gray scale images.
Binary morphological operations
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This application performs binary morphological operations (dilation,
erosion, opening and closing) on a mono band image with a specific
structuring element (a ball or a cross) having one radius along X and
another one along Y. NB: the cross shaped structuring element has a
fixed radius equal to 1 pixel in both X and Y directions.
The *BinaryMorphologicalOperation* application has the following input
parameters:
-``-in`` the input image to be filtered
-``-channel`` the selected channel index in the input image to be
processed (default value is 1)
-``-structype`` the choice of the structuring element type
(ball/cross) (default value is ball)
-``(-structype.ball.xradius)`` the ball structuring element X Radius
(only if structype==ball) (default value is 5 pixels)
-``(-structype.ball.yradius)`` the ball structuring element Y Radius
(only if structype==ball) (default value is 5 pixels)
-``-filter`` the choice of the morphological operation
(dilate/erode/opening/closing) (default value is dilate)
-``(-filter.dilate.foreval)`` the foreground value for the dilation
(idem for filter.erode/opening/closing) (default value is 1)
-``(-filter.dilate.backval)`` the background value for the dilation
(idem for filter.erode/opening/closing) (default value is 0)
-``-out`` the output filtered image
The application can be used like this:
::
otbcli_BinaryMorphologicalOperation -in InputImage
-channel 1
-structype ball
-structype.ball.xradius 10
-structype.ball.yradius 5
-filter opening
-filter.opening.foreval 1.0
-filter.opening.backval 0.0
-out OutputImage
Gray scale morphological operations
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This application performs morphological operations (dilation, erosion,
opening and closing) on a gray scale mono band image with a specific
structuring element (a ball or a cross) having one radius along X and
another one along Y. NB: the cross shaped structuring element has a
fixed radius equal to 1 pixel in both X and Y directions.
The *GrayScaleMorphologicalOperation* application has the following
input parameters:
-``-in`` the input image to be filtered
-``-channel`` the selected channel index in the input image to be
processed (default value is 1)
-``-structype`` the choice of the structuring element type
(ball/cross) (default value is ball)
-``(-structype.ball.xradius)`` the ball structuring element X Radius
(only if structype==ball) (default value is 5 pixels)
-``(-structype.ball.yradius)`` the ball structuring element Y Radius
(only if structype==ball) (default value is 5 pixels)
-``-filter`` the choice of the morphological operation
(dilate/erode/opening/closing) (default value is dilate)
-``-out`` the output filtered image
The application can be used like this:
::
otbcli_GrayScaleMorphologicalOperation -in InputImage
-channel 1
-structype ball
-structype.ball.xradius 10
-structype.ball.yradius 5
-filter opening
-out OutputImage
Textural features extraction
----------------------------
Texture features can be extracted with the help of image filters based
on texture analysis methods like Haralick and structural feature set
(SFS).
Haralick texture features
~~~~~~~~~~~~~~~~~~~~~~~~~
This application computes Haralick, advanced and higher order texture
features on every pixel in the selected channel of the input image. The
output image is multi band with a feature per band.
The *HaralickTextureExtraction* application has the following input
parameters:
-``-in`` the input image to compute the features on
-``-channel`` the selected channel index in the input image to be
processed (default value is 1)
-``-texture`` the texture set selection [simple/advanced/higher]
(default value is simple)
-``-parameters.min`` the input image minimum (default value is 0)
-``-parameters.max`` the input image maximum (default value is 255)
-``-parameters.xrad`` the X Radius of the processing neighborhood
(default value is 2 pixels)
-``-parameters.yrad`` the Y Radius of the processing neighborhood
(default value is 2 pixels)
-``-parameters.xoff`` the :math:`\Delta`\ X Offset for the
co-occurrence computation (default value is 1 pixel)
-``-parameters.yoff`` the :math:`\Delta`\ Y Offset for the
co-occurrence computation (default value is 1 pixel)
-``-parameters.nbbin`` the number of bin per axis for histogram
generation (default value is 8)
-``-out`` the output multi band image containing the selected texture
features (one feature per band)
The available values for -texture with their relevant features are:
-``-texture=simple:`` In this case, 8 local Haralick textures features
will be processed. The 8 output image channels are: Energy, Entropy,
Correlation, Inverse Difference Moment, Inertia, Cluster Shade,
Cluster Prominence and Haralick Correlation. They are provided in
this exact order in the output image. Thus, this application computes
the following Haralick textures over a neighborhood with user defined
radius. To improve the speed of computation, a variant of Grey Level
Co-occurrence Matrix(GLCM) called Grey Level Co-occurrence Indexed
List (GLCIL) is used. Given below is the mathematical explanation on
the computation of each textures. Here :math:`g(i, j)` is the
frequency of element in the GLCIL whose index is i, j. GLCIL stores a
pair of frequency of two pixels taken from the given offset and the
cell index (i, j) of the pixel in the neighborhood window. :(where
each element in GLCIL is a pair of pixel index and it’s frequency,
:math:`g(i, j)` is the frequency value of the pair having index is
i, j).
“Energy†:math:`= f_1 = \sum_{i, j}g(i, j)^2`
“Entropy†:math:`= f_2 = -\sum_{i, j}g(i, j) \log_2 g(i, j)`, or 0
if :math:`g(i, j) = 0`
“Correlationâ€
:math:`= f_3 = \sum_{i, j}\frac{(i - \mu)(j - \mu)g(i, j)}{\sigma^2}`
“Inverse Difference Momentâ€
:math:`= f_4 = \sum_{i, j}\frac{1}{1 + (i - j)^2}g(i, j)`
“Inertia†:math:`= f_5 = \sum_{i, j}(i - j)^2g(i, j)` (sometimes
called “contrastâ€)
“Cluster Shadeâ€
:math:`= f_6 = \sum_{i, j}((i - \mu) + (j - \mu))^3 g(i, j)`
“Cluster Prominenceâ€
:math:`= f_7 = \sum_{i, j}((i - \mu) + (j - \mu))^4 g(i, j)`
“Haralick’s Correlationâ€
:math:`= f_8 = \frac{\sum_{i, j}(i, j) g(i, j) -\mu_t^2}{\sigma_t^2}`
where :math:`\mu_t` and :math:`\sigma_t` are the mean and standard
deviation of the row (or column, due to symmetry) sums. Above,
:math:`\mu =` (weighted pixel average)
:math:`= \sum_{i, j}i \cdot g(i, j) = \sum_{i, j}j \cdot g(i, j)`
(due to matrix symmetry), and :math:`\sigma =` (weighted pixel
variance)
:math:`= \sum_{i, j}(i - \mu)^2 \cdot g(i, j) = \sum_{i, j}(j - \mu)^2 \cdot g(i, j)`
(due to matrix symmetry).
-``-texture=advanced:`` In this case, 10 advanced texture features
will be processed. The 10 output image channels are: Mean, Variance,
Dissimilarity, Sum Average, Sum Variance, Sum Entropy, Difference of
Entropies, Difference of Variances, IC1 and IC2. They are provided in
this exact order in the output image. The textures are computed over
a sliding window with user defined radius.
To improve the speed of computation, a variant of Grey Level
Co-occurrence Matrix(GLCM) called Grey Level Co-occurrence Indexed
List (GLCIL) is used. Given below is the mathematical explanation on
the computation of each textures. Here :math:`g(i, j)` is the
frequency of element in the GLCIL whose index is i, j. GLCIL stores a
pair of frequency of two pixels taken from the given offset and the
cell index (i, j) of the pixel in the neighborhood window. :(where
each element in GLCIL is a pair of pixel index and it’s frequency,
:math:`g(i, j)` is the frequency value of the pair having index is
i, j).
“Mean†:math:`= \sum_{i, j}i g(i, j)`
“Sum of squares: Varianceâ€
:math:`= f_4 = \sum_{i, j}(i - \mu)^2 g(i, j)`
“Dissimilarity†:math:`= f_5 = \sum_{i, j}(i - j) g(i, j)^2`
“Sum average†:math:`= f_6 = -\sum_{i}i g_{x+y}(i)`
“Sum Variance†:math:`= f_7 = \sum_{i}(i - f_8)^2 g_{x+y}(i)`
“Sum Entropy†:math:`= f_8 = -\sum_{i}g_{x+y}(i) log (g_{x+y}(i))`
“Difference variance†:math:`= f_10 = variance of g_{x-y}(i)`
“Difference entropyâ€
:math:`= f_11 = -\sum_{i}g_{x-y}(i) log (g_{x-y}(i))`
“Information Measures of Correlation IC1â€
:math:`= f_12 = \frac{f_9 - HXY1}{H}`
“Information Measures of Correlation IC2â€
:math:`= f_13 = \sqrt{1 - \exp{-2}|HXY2 - f_9|}`
Above, :math:`\mu =` (weighted pixel average)
:math:`= \sum_{i, j}i \cdot g(i, j) = \sum_{i, j}j \cdot g(i, j)`
(due to matrix summetry), and
:math:`g_{x+y}(k) = \sum_{i}\sum_{j}g(i)` where :math:`i+j=k`
and :math:`k = 2, 3, .., 2N_{g}` and
:math:`g_{x-y}(k) = \sum_{i}\sum_{j}g(i)` where :math:`i-j=k`
and :math:`k = 0, 1, .., N_{g}-1`
-``-texture=higher:`` In this case, 11 local higher order statistics
texture coefficients based on the grey level run-length matrix will
be processed. The 11 output image channels are: Short Run Emphasis,
Long Run Emphasis, Grey-Level Nonuniformity, Run Length
Nonuniformity, Run Percentage, Low Grey-Level Run Emphasis, High
Grey-Level Run Emphasis, Short Run Low Grey-Level Emphasis, Short Run
High Grey-Level Emphasis, Long Run Low Grey-Level Emphasis and Long
Run High Grey-Level Emphasis. They are provided in this exact order
in the output image. Thus, this application computes the following
Haralick textures over a sliding window with user defined radius:
(where :math:`p(i, j)` is the element in cell i, j of a normalized
Run Length Matrix, :math:`n_r` is the total number of runs and
:math:`n_p` is the total number of pixels):
“Short Run Emphasisâ€
:math:`= SRE = \frac{1}{n_r} \sum_{i, j}\frac{p(i, j)}{j^2}`
“Long Run Emphasisâ€
:math:`= LRE = \frac{1}{n_r} \sum_{i, j}p(i, j) * j^2`
“Grey-Level Nonuniformityâ€
:math:`= GLN = \frac{1}{n_r} \sum_{i} \left( \sum_{j}{p(i, j)} \right)^2`
“Run Length Nonuniformityâ€
:math:`= RLN = \frac{1}{n_r} \sum_{j} \left( \sum_{i}{p(i, j)} \right)^2`
“Run Percentage†:math:`= RP = \frac{n_r}{n_p}`
“Low Grey-Level Run Emphasisâ€
:math:`= LGRE = \frac{1}{n_r} \sum_{i, j}\frac{p(i, j)}{i^2}`
“High Grey-Level Run Emphasisâ€
:math:`= HGRE = \frac{1}{n_r} \sum_{i, j}p(i, j) * i^2`
“Short Run Low Grey-Level Emphasisâ€
:math:`= SRLGE = \frac{1}{n_r} \sum_{i, j}\frac{p(i, j)}{i^2 j^2}`
“Short Run High Grey-Level Emphasisâ€
:math:`= SRHGE = \frac{1}{n_r} \sum_{i, j}\frac{p(i, j) * i^2}{j^2}`
“Long Run Low Grey-Level Emphasisâ€
:math:`= LRLGE = \frac{1}{n_r} \sum_{i, j}\frac{p(i, j) * j^2}{i^2}`
“Long Run High Grey-Level Emphasisâ€
:math:`= LRHGE = \frac{1}{n_r} \sum_{i, j} p(i, j) i^2 j^2`
The application can be used like this:
::
otbcli_HaralickTextureExtraction -in InputImage
-channel 1
-texture simple
-parameters.min 0
-parameters.max 255
-out OutputImage
SFS texture extraction
~~~~~~~~~~~~~~~~~~~~~~
This application computes Structural Feature Set textures on every pixel
in the selected channel of the input image. The output image is multi
band with a feature per band. The 6 output texture features are
SFS’Length, SFS’Width, SFS’PSI, SFS’W-Mean, SFS’Ratio and SFS’SD. They
are provided in this exact order in the output image.
It is based on line direction estimation and described in the following
publication. Please refer to Xin Huang, Liangpei Zhang and Pingxiang Li
publication, Classification and Extraction of Spatial Features in Urban
Areas Using High-Resolution Multispectral Imagery. IEEE Geoscience and
Remote Sensing Letters, vol. 4, n. 2, 2007, pp 260-264.
The texture is computed for each pixel using its neighborhood. User can
set the spatial threshold that is the max line length, the spectral
threshold that is the max difference authorized between a pixel of the
line and the center pixel of the current neighborhood. The adjustement
constant alpha and the ratio Maximum Consideration Number, which
describes the shape contour around the central pixel, are used to
compute the :math:`w - mean` value.
The *SFSTextureExtraction* application has the following input
parameters:
-``-in`` the input image to compute the features on
-``-channel`` the selected channel index in the input image to be
processed (default value is 1)
-``-parameters.spethre`` the spectral threshold (default value is 50)
-``-parameters.spathre`` the spatial threshold (default value is 100
pixels)
-``-parameters.nbdir`` the number of directions (default value is 20)
-``-parameters.alpha`` the alpha value (default value is 1)
-``-parameters.maxcons`` the ratio Maximum Consideration Number
(default value is 5)
-``-out`` the output multi band image containing the selected texture
features (one feature per band)
The application can be used like this:
::
otbcli_SFSTextureExtraction -in InputImage
-channel 1
-out OutputImage