SetDescription("Filters the input labeled image using Majority Voting in a ball shaped neighbordhood.");
SetDescription("Filters the input labeled image using Majority Voting in a ball shaped neighbordhood");
SetDocName("Classification Map Regularization");
SetDocLongDescription("This application filters the input labeled image (with a maximal class label = 65535) using Majority Voting in a ball shaped neighbordhood. Majority Voting takes the more representative value of all the pixels identified by the ball shaped structuring element and then sets the center pixel to this majority label value.\n\
-NoData is the label of the NOT classified pixels in the input image. These input pixels keep their NoData label in the output image.\n\
-Pixels with more than 1 majority class are marked as Undecided if the parameter 'ip.suvbool == true', or keep their Original labels otherwise.");
SetDocLongDescription(
"This application filters the input labeled image (with a maximal class label = 65535) using Majority Voting in a ball shaped neighbordhood."
"Majority Voting takes the more representative value of all the pixels identified by the ball shaped structuring element and then sets the center pixel to this majority label value.\n\n"
"* NoData is the label of the NOT classified pixels in the input image. These input pixels keep their NoData label in the output image.\n\n"
"* Pixels with more than 1 majority class are marked as Undecided if the parameter 'ip.suvbool == true', or keep their Original labels otherwise.");
SetDocLimitations("The input image must be a single band labeled image (with a maximal class label = 65535). The structuring element radius must have a minimum value equal to 1 pixel. Please note that the Undecided value must be different from existing labels in the input labeled image.");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("Documentation of the ClassificationMapRegularization application.");
"Several different methods are used to eliminate speckle noise, based upon"
" different mathematical models of the phenomenon. The application includes four"
" methods: Lee [1], Frost [2], GammaMAP [3] and Kuan [4]. \n\n"
"We sum up below the basic principle of this four methods:\n"
" * Lee : Estimate the signal by mean square error minimization (MMSE) on a sliding window.\n"
" * Frost : Also derived from the MMSE criteria with a weighted sum of the values within the window. The weighting factors decrease with distance from the pixel of interest.\n"
" * GammaMAP : Derived under the assumption of the image follows a Gamma distribution.\n"
" * Kuan : Also derived from the MMSE criteria under the assumption of non stationary mean and variance. It is quite similar to Lee filter in form."
"We sum up below the basic principle of this four methods:\n\n"
"* Lee : Estimate the signal by mean square error minimization (MMSE) on a sliding window.\n"
"* Frost : Also derived from the MMSE criteria with a weighted sum of the values within the window. The weighting factors decrease with distance from the pixel of interest.\n"
"* GammaMAP : Derived under the assumption of the image follows a Gamma distribution.\n"
"* Kuan : Also derived from the MMSE criteria under the assumption of non stationary mean and variance. It is quite similar to Lee filter in form."
);
SetDocLimitations("The application does not handle complex image as input.");
SetDescription("Reproject and rasterize a vector dataset");
SetDocName("Rasterization");
SetDocLongDescription("This application allows reprojecting and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset.\n There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry with respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.");
SetDocLongDescription(
"Reproject and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by "
"setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other "
"parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset.\n\n"
"There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the "
"input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry "
"with respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value "
"is still used for unassociated pixels.");
SetDocLimitations("None");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("For now, support of input dataset with multiple layers having different projection reference system is limited.");
