Commit 6bb14615 authored by remicres's avatar remicres
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DOC: Add more documentation to applications

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......@@ -103,7 +103,11 @@ public:
"dNDVI on masked pixels. 3) Apply multiple thresholds based on "
"computed stats on the dNDVI image to obtain a label image which "
"quantize NDVI decrease. Input images must be in the same projection "
"but might have different origin/size (A NN-interpolation is done).");
"but might have different origin/size (A NN-interpolation is done). "
"Outputs are dNDVI (out) and classes labels (outlabel). Classes "
"are the following: 0 is no-data, 1 is no vegetation decrease, "
"2 is little vegetation decrease, 3 is medium vegetation decrease, "
"and 4 is high vegetation decrease, that is, clear cuts.");
AddDocTag(Tags::FeatureExtraction);
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......@@ -114,21 +114,25 @@ public:
// Documentation
SetDocName("ClearCutsDetectionFromNDVIStack");
SetDescription("This application performs harvest detection, "
"from two stacks of NDVI (or another vegetation indices) an optional forest mask");
"from two stacks of NDVI (or another vegetation indices) "
"and an optional forest mask. Input images must be in "
"the same geometry (crs/origin/size/spacing)");
SetDocLimitations("None");
SetDocAuthors("RemiCresson");
SetDocLongDescription(" This filter implements the clear cut detection method, based "
"on the work of Kenji Ose and Michel Deshayes at IRSTEA. "
"Steps of the process are the following: 1) Compute the difference "
"between NDVI of dates t1 and t0. 2) compute mean and std of the "
"dNDVI on masked pixels. 3) Apply multiple thresholds based on "
"computed stats on the dNDVI image to obtain a label image which "
"quantize NDVI decrease. The application can use an input mask for vegetation. "
"between NDVI of first valid pixel of input image stacks. "
"2) compute mean and std of the dNDVI on masked pixels. "
"3) Apply multiple thresholds based on computed stats on the "
"dNDVI image to obtain a label image which quantize NDVI decrease. "
"The application can use an input mask for vegetation. "
"An image of classes labels is produced (0: no detection, 1: high probability "
"of detection, 2: very high probability of detection) and also an "
"output raster of the used images indices of the input stack. "
"Input images indices are encoded in 8 bits, and the registry (File "
"that associates file names and its value) can be saved on disk in CSV format.");
"that associates used images file names with index values) can be "
"saved on disk in CSV format.");
AddDocTag(Tags::FeatureExtraction);
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