Commit 8272fe90 authored by Cédric Traizet's avatar Cédric Traizet

DOC: fixed documentation

parent 062b0ffc
......@@ -60,22 +60,22 @@ private:
SetDocLongDescription("This application estimates the number of endmembers "
"in a hyperspectral image. It first computes statistics on the image and then "
"apply an endmember number estimation algorithm using these statistics. Two "
"algorithms are available: \n"
"1) Virtual Dimensionality (vd) [1][2] \n"
"2) Eigenvalue Likelihood Maximization (elm) [3][4] \n"
"The application then returns the estimated number of endmembers. \n"
"References : \n"
"algorithms are available: \n\n "
" 1) Virtual Dimensionality (VD) [1] [2] \n "
" 2) Eigenvalue Likelihood Maximization (ELM) [3][4] \n"
"The application then returns the estimated number of endmembers. \n\n "
"References : \n\n "
"[1] C.-I. Chang and Q. Du, Estimation of number of spectrally distinct signal "
"sources in hyperspectral imagery, IEEE Transactions on Geoscience and Remote "
"Sensing, vol. 43, no. 3, mar 2004. \n"
"Sensing, vol. 43, no. 3, mar 2004. \n "
"[2] J. Wang and C.-I. Chang, Applications of independent component analysis "
"in endmember extraction and abundance quantification for hyperspectral imagery"
", IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 9, pp. "
"2601-1616, sep 2006. \n"
"2601-1616, sep 2006. \n "
"[3] Unsupervised Endmember Extraction of Martian Hyperspectral Images, B.Luo, "
"J. Chanussot, S. Dout\'e and X. Ceamanos, IEEE Whispers 2009, Grenoble France, 2009 \n"
"J. Chanussot, S. Dout\'e and X. Ceamanos, IEEE Whispers 2009, Grenoble France, 2009 \n "
"[4] Unsupervised classification of hyperspectral images by using "
"linear unmixing algorithm Luo, B. and Chanussot, J., IEEE Int. Conf. On Image \n"
"linear unmixing algorithm Luo, B. and Chanussot, J., IEEE Int. Conf. On Image"
"Processing(ICIP) 2009, Cairo, Egypte, 2009"
);
......@@ -85,20 +85,19 @@ private:
AddDocTag(Tags::Hyperspectral);
AddParameter(ParameterType_InputImage, "in", "Input Image Filename");
AddParameter(ParameterType_InputImage, "in", "Input Image Filename");
SetParameterDescription("in","The hyperspectral data cube input");
AddParameter(ParameterType_Choice, "algo", "Unmixing algorithm");
SetParameterDescription("algo", "The algorithm to use for the estimation");
AddChoice("algo.elm", "elm");
SetParameterDescription("algo.elm", "Eigenvalue Likelihood Maximization");
SetParameterDescription("algo.elm", "Eigenvalue Likelihood Maximization algorithm");
AddChoice("algo.vd", "vd");
SetParameterDescription("algo.vd", "virtual dimensionality");
SetParameterDescription("algo.vd", "Virtual Dimensionality algorithm");
AddParameter( ParameterType_Float , "algo.vd.far" , "false alarm rate"
"Maximum training predictors");
SetMinimumParameterFloatValue("algo.vd.far",0);
SetMaximumParameterFloatValue("algo.vd.far",1);
AddParameter( ParameterType_Float , "algo.vd.far" , "False alarm rate");
SetMinimumParameterFloatValue("algo.vd.far", 0);
SetMaximumParameterFloatValue("algo.vd.far", 1);
SetDefaultParameterFloat( "algo.vd.far" , 1.0E-3 );
SetParameterDescription( "algo.vd.far" ,
"False alarm rate for the virtual dimensionality algorithm");
......
......@@ -95,13 +95,13 @@ private:
// Documentation
SetDocName("Hyperspectral data unmixing");
SetDocLongDescription("The application applies a linear unmixing algorithm"
"to an hyperspectral data cube. This method supposes that the mixture between"
"aterials in the scene is macroscopic and simulates a linear mixing model of"
SetDocLongDescription("The application applies a linear unmixing algorithm "
"to an hyperspectral data cube. This method supposes that the mixture between "
"aterials in the scene is macroscopic and simulates a linear mixing model of "
"spectra.\n\n"
"The Linear Mixing Model (LMM) acknowledges that reflectance"
"spectrum associated with each pixel is a linear combination of pure"
"materials in the recovery area, commonly known as endmembers. Endmembers can"
"The Linear Mixing Model (LMM) acknowledges that reflectance "
"spectrum associated with each pixel is a linear combination of pure "
"materials in the recovery area, commonly known as endmembers. Endmembers can "
"be estimated using the VertexComponentAnalysis application.\n\n"
"The application allows estimating the abundance maps with several algorithms:\n\n"
"* Unconstrained Least Square (ucls)\n"
......@@ -121,10 +121,14 @@ private:
SetParameterDescription("in","The hyperspectral data cube input");
AddParameter(ParameterType_OutputImage, "out", "Output Image");
SetParameterDescription("out","The output abundance map. The abundance fraction are stored in a multispectral image where band N corresponds to the fraction of endmembers N in each pixel.");
SetParameterDescription("out","The output abundance map. The abundance "
"fraction are stored in a multispectral image where band N corresponds "
"to the fraction of endmembers N in each pixel.");
AddParameter(ParameterType_InputImage, "ie", "Input endmembers");
SetParameterDescription("ie","The endmembers (estimated pure pixels) to use for unmixing. Must be stored as a multispectral image, where each pixel is interpreted as an endmember.");
SetParameterDescription("ie","The endmembers (estimated pure pixels) to "
"use for unmixing. Must be stored as a multispectral image, where "
"each pixel is interpreted as an endmember.");
AddParameter(ParameterType_Choice, "ua", "Unmixing algorithm");
SetParameterDescription("ua", "The algorithm to use for unmixing");
......
......@@ -50,25 +50,25 @@ private:
void DoInit() override
{
SetName("VertexComponentAnalysis");
SetDescription("Given a set of mixed spectral vectors, estimate"
"reference substances also known as endmembers using the Vertex"
SetDescription("Given a set of mixed spectral vectors, estimate "
"reference substances also known as endmembers using the Vertex "
"Component Analysis algorithm.");
// Documentation
SetDocName("Vertex Component Analysis");
SetDocLongDescription("Apply the Vertex Component Analysis [1] to"
"an hyperspectral image to extract endmembers. Given a set of mixed"
"spectral vectors (multispectral or hyperspectral), the application"
"estimates the spectral signature of reference substances also known"
SetDocLongDescription("Apply the Vertex Component Analysis [1] to "
"an hyperspectral image to extract endmembers. Given a set of mixed "
"spectral vectors (multispectral or hyperspectral), the application "
"estimates the spectral signature of reference substances also known "
"as endmembers.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("[1] J. M. P. Nascimento and J. M. B. Dias, Vertex"
"component analysis: a fast algorithm to unmix hyperspectral data,"
"in IEEE Transactions on Geoscience and Remote Sensing, vol. 43,"
"no. 4, pp. 898-910, April 2005.J. M. P. Nascimento and"
"J. M. B. Dias, Vertex component analysis: a fast algorithm to"
"unmix hyperspectral data, in IEEE Transactions on Geoscience and"
SetDocSeeAlso("[1] J. M. P. Nascimento and J. M. B. Dias, Vertex "
"component analysis: a fast algorithm to unmix hyperspectral data, "
"in IEEE Transactions on Geoscience and Remote Sensing, vol. 43, "
"no. 4, pp. 898-910, April 2005.J. M. P. Nascimento and "
"J. M. B. Dias, Vertex component analysis: a fast algorithm to "
"unmix hyperspectral data, in IEEE Transactions on Geoscience and "
"Remote Sensing, vol. 43, no. 4, pp. 898-910, April 2005.");
AddDocTag(Tags::Hyperspectral);
......@@ -78,9 +78,9 @@ private:
SetParameterDescription("in","Input hyperspectral data cube");
AddParameter(ParameterType_OutputImage, "outendm", "Output Endmembers");
SetParameterDescription("outendm","Endmembers, stored in a"
"one-line multi-spectral image.Each pixel corresponds to one"
"endmembers and each band values corresponds to the spectral"
SetParameterDescription("outendm","Endmembers, stored in a "
"one-line multi-spectral image.Each pixel corresponds to one "
"endmembers and each band values corresponds to the spectral "
"signature of the corresponding endmember.");
MandatoryOn("outendm");
......
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