Mosaic

Perform a mosaic of input images

Description

This application performs a mosaic of the input images

Parameters

Input Images -il image1 image2... Mandatory
Input images to mosaic

Input VectorData for composition -vdcut vectorfile1 vectorfile2...
VectorData files to be used for cut input images. Must be provided in the same order as the input images.

Input VectorData for statistics -vdstats vectorfile1 vectorfile2...
VectorData files to be used for statistics computation (harmonization). Must be provided in the same order as the input images.

Mosaic compositing mode

This group of parameters sets the mosaic compositing

Feathering method -comp.feather [none|large|slim] Default value: none
Set the feathering method for composition

  • The simplest composition mode
    No feathering method is used. Images are stacked in overlaps
  • The large blending composition mode
    Blends all images on the maximum overlapping areas. May generate blur when inputs are not well aligned
  • The slim blending composition mode
    Blends the last image over earlier ones in areas of overlap, on a given transition distance

The slim blending composition mode options

Transition smoothness (1.0 = linear transition) -comp.feather.slim.exponent float Default value: 1

Transition length (In cartographic units) -comp.feather.slim.lenght float

Spectral bands harmonization mode

This group of parameters tunes the mosaic harmonization

harmonization method -harmo.method [none|band|rgb] Default value: none
Set the harmonization method

  • None
    No automatic harmonization is done
  • Spectral bands
    Automatic harmonization is performed on each individual spectral band
  • True colors
    Works on RGB colors only. Use an harmonization method based on quadratic programming in LAB color space

harmonization cost function -harmo.cost [rmse|musig|mu] Default value: rmse
Set the harmonization cost function

  • RMSE based
  • Mean+Std based
  • Mean based

Output image -out image [dtype] Mandatory
Output image, resulting from the mosaicing process.

Interpolation -interpolator [nn|bco|linear] Default value: nn
This group of parameters allows to define how the input image will be interpolated during resampling.

  • Nearest Neighbor interpolation
    Nearest neighbor interpolation leads to poor image quality, but it is fast.
  • Bicubic interpolation
    Bicubic interpolation leads to great image quality, but it is slow.
  • Linear interpolation
    Linear interpolation leads to average image quality but is quite fast

Bicubic interpolation options

Radius for bicubic interpolation -interpolator.bco.radius int Default value: 2
Size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artefacts.

Output Image Grid

Output mosaic pixel grid parameters

Pixel Size (X) -output.spacingx float
Physical size (X) of output image pixels, in cartographic units.

Pixel Size (Y) -output.spacingy float
Physical size (Y) of output image pixels, in cartographic units..

Directory for temporary files -tmpdir directory
Directory for temporary files. If not set, the output image parent directory is used.

Distance maps computation

Distance maps sampling ratio -distancemap.sr float Default value: 10
Distance maps are computed when the compositing method uses feathering. The distance maps sampling ratio is the ratio between the distance map physical spacing and the physical spacing of the original image. It is used to change the distance maps physical spacing: since the distance map computation is not a streamable pipeline, it can be useful to compute slightly smaller distance maps. distancemap.sr can be hence increased if input images are too big to fit the RAM, or in order to speed up the process

no-data value -nodata float Default value: 0
The no-data value is ignored during statistics computations (for color harmonisation) and for the mosaic compositing.

Available RAM (MB) -ram int Default value: 256
Available memory for processing (in MB).

Examples

From the command-line:

otbcli_Mosaic -il SP67_FR_subset_1.tif SP67_FR_subset_2.tif -out mosaicImage.tif

From Python:

import otbApplication

app = otbApplication.Registry.CreateApplication("Mosaic")

app.SetParameterStringList("il", ['SP67_FR_subset_1.tif', 'SP67_FR_subset_2.tif'])
app.SetParameterString("out", "mosaicImage.tif")

app.ExecuteAndWriteOutput()

Limitations

  1. When “comp” parameter is different than “none”, the sampling ratio for distance map computation can be adjusted to make input images fit into memory (distance map computation is not streamable).2. When “harmo” method is not “none”, an algorithm performs the color harmonization of the input images using quadratic programming (QP). The objective function of the QP is a set of matrices, each one with size NxN (N being the number of input images). Hence, a large number of input images imply that those matrices fit into memory, and this is not related to the streaming capability of the application (ram parameter) but intrinsic to the harmonization process.