diff --git a/Documentation/Cookbook/rst/recipes/hyperspectral.rst b/Documentation/Cookbook/rst/recipes/hyperspectral.rst
index 5e505732171907af801a99bac073b71293d356fc..c3251861eeee476b78093eea16c0f7b3b5e38b61 100644
--- a/Documentation/Cookbook/rst/recipes/hyperspectral.rst
+++ b/Documentation/Cookbook/rst/recipes/hyperspectral.rst
@@ -1,37 +1,102 @@
Hyperspectral image processing
==============================
+Hyperspectral sensors produce images with hundreds of narrow spectral bands
+within the visible, near-infrared, mid-infrared and short wave infrared portions
+of the electromagnetic spectrum. This means that an entire spectrum is acquired
+at each point. The richness of this information is useful in many applications,
+like geology and agriculture.
+
+This section presents OTB applications for hyperspectral image processing.
+
Unmixing
--------
+Because of the low spatial resolution of hyperspectral sensors, microscopic
+material mixing and multiple scattering, measured spectra are a mixture
+of spectra of the materials actually in the scene. Thus, pixels are assumed
+to be mixtures of a few materials, called endmembers. In this section the mixing
+is assumed to be linear, i.e.
+
+.. math::
+ R = A.S + N
+
+where, if `l` is the number of bands of the hyperspectral image, `n` the number of pixels
+and `k` the number of endmember :
+
+- :math:`R` is the matrix of observed pixel, of size `l.n`
+- :math:`A` is the endmember matrix, of size `l.k`
+- :math:`S` is the abundance matrix, of size `k.n`
+- :math:`N` is the noise matrix, of size `l.n`
+
+The unmixing problem is to estimate matrices :math:`A` and :math:`S` from :math:`R`.
+The following presents an example of hyperspectral unmixing on a scene composed
+of several geological materials. The scene is an extract from the Cuprite dataset
+acquired by the AVIRIS sensor. The extract is available here_, the whole dataset
+can be retrieved from AVIRIS_ NASA site.
+
+
.. figure:: ../Art/HyperspectralImages/cuprite_rgb.png
+ :width: 70%
+ :align: center
Cuprite image considered, here bands 16, 100 and 180 are displayed.
+As the number of endmembers of the input image is unknown, the first step
+of the hyperspectral unmixing is to estimate this number.
::
- otbcli_EndmemberNumberEstimation -in inputImage
+ otbcli_EndmemberNumberEstimation -in inputImage.tif
-algo vd
-algo.vd.far 1e-5
+Using a HFC virtual dimensionality algorithm, the output is:
+
+::
+
+ Output parameters value:
+ number: 19
+
+The next step of the hyperspectral is to estimate the endmembers. This can
+be done using the Vertex Component Analysis algorithm (VCA).
::
- otbcli_VertexComponentAnalysis -in inputImage
+ otbcli_VertexComponentAnalysis -in inputImage.tif
-ne 19
-outendm endmembers.tif
+The output of the application is an image containing `k` pixels, each pixel
+representing an endmember.
+
+This algorithm is based on the research of the endmembers among the data.
+This means that a minimum of one pure pixel must be associated with each endmembers.
+The rationale behind this idea is that the hyperspectral mixed data is contained
+in a simplex of dimension `k`, where the endmembers are the edges of the simplex.
+This algorithm is widely used in hyperspectral unmixing because of its low algorithmic
+complexity and the fact that the endmember estimation is unbiased in absence of noise.
+However if the pure pixel hypothesis is not respected, there will be an estimation
+error on the endmembers.
+
+The last step of the unmixing is to estimates the abundance matrix using an
+optimization algorithm.
+
::
- otbcli_HyperspectralUnmixing -in inputImage
+ otbcli_HyperspectralUnmixing -in inputImage.tif
-ie endmembers.tif
-out unmixedImage.tif
-ua ucls
+Here an unconstrained least square algorithm has been used. The resulting abundance
+image is shown belove:
+
.. figure:: ../Art/HyperspectralImages/hyperspectralUnmixing_rgb.png
+ :width: 70%
+ :align: center
Resulting unmixed image, here the first three bands are displayed.
@@ -39,18 +104,40 @@ Unmixing
Anomaly detection
-----------------
+An anomaly is an element that is not expected to be found in a scene. The unusual element
+is likely different from its environment and its presence is in the minority scene. Typically,
+a rock in a field, a wooden hut in a forest are anomalies that can be desired to detect using
+hyperspectral imagery. Here an anomaly detection algorithm, the local Rx
+detector, will be used to detect small objects in urban environment. The input image
+has been acquired over Pavia_ University by the ROSIS sensor and has 103 spectral bands.
+
.. figure:: ../Art/HyperspectralImages/pavia.png
:width: 70%
+ :align: center
+
+ Hyperspectral image of the Pavia university, here bands 50, 30 and 10 are displayed.
- Hyperspectral image of the Pavia university.
+
+A first optional step is to reduce the dimensionality of the input image in order to
+reduce the size of spectral data while maintaining information related to
+anomalies, here a Principal Component Analysis algorithm is used.
::
- otbcli_DimensionalityReduction -in ${PAVIA}
+ otbcli_DimensionalityReduction -in inputImage.tif
-out reducedData.tif
-method pca
-nbcomp 10
+
+As the local Rx needs to compute correlation matrices, applying dimensionality reduction
+as a preprocessing step will significantly reduce the computational cost of the algorithm.
+
+The local Rx detection use a sliding window to compute an anomaly score on each pixel.
+This window consists of two sub windows, of radius `ir` and `er`, with `ir < er`,
+and centered on the pixel of interest. The local Rx score is computed by comparing the
+center pixel with the pixel belonging to the annulus.
+
::
otbcli_LocalRxDetection -in reducedData.tif
@@ -58,6 +145,14 @@ Anomaly detection
-ir 1
-er 5
+Here anomalies are supposed to be small, hence a small internal radius should be chosen e.g.
+`ir=1`. Also as the environment is urban, the statistic of the background varies rapidly with the distance,
+so the external radius should not be too big, here `er=5` has been chosen.
+These parameters really depend on the input image and on the objects of interest.
+
+A threshold can then be applied on the resulting score image to produce an
+anomaly map.
+
::
otbcli_BandMath -il RxScore.tif
@@ -65,6 +160,7 @@ Anomaly detection
-exp "im1b1>100"
+The value of the threshold depends on how sensitive the anomaly detector should be.
.. |image_1| image:: ../Art/HyperspectralImages/rx_score.png
@@ -80,3 +176,6 @@ Anomaly detection
Left: Computed Rx score, right: detected anomalies (in red)
+.. _here: http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Cuprite
+.. _AVIRIS: https://aviris.jpl.nasa.gov/
+.. _Pavia: http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Pavia_University_scene