How to…

Get full stereo products

Dinamis

DINAMIS is a platform that acquires and distributes satellite Earth imagery for French and foreign institutional users under specific subscription conditions.

Please visit the dinamis website for more information: https://dinamis.data-terra.org/.

AIRBUS Pleiades NEO example files

Example files are available here: https://intelligence.airbus.com/imagery/sample-imagery/pleiades-neo-tristereo-marseille/ (A form must be filled out to access the data).

Maxar WorldView example files

Example files are available on AWS S3 through the SpaceNet challenge here: s3://spacenet-dataset/Hosted-Datasets/MVS_dataset/WV3/PAN/.

You need to install aws-cli:

python -m venv venv-aws-cli # create a virtual environment
source ./venv-aws-cli/bin/activate # activate it
pip install --upgrade pip # upgrade pip
pip install awscli

And download a stereo:

aws s3 cp --no-sign-request s3://spacenet-dataset/Hosted-Datasets/MVS_dataset/WV3/PAN/18DEC15WV031000015DEC18140522-P1BS-500515572020_01_P001_________AAE_0AAAAABPABJ0.NTF .
aws s3 cp --no-sign-request s3://spacenet-dataset/Hosted-Datasets/MVS_dataset/WV3/PAN/18DEC15WV031000015DEC18140554-P1BS-500515572030_01_P001_________AAE_0AAAAABPABJ0.NTF  .

Prepare input images

Make input ROI images

cars-extractroi script allows to extract region of interest from your image product.

usage: cars-extractroi [-h] -il [IL [IL ...]] -out OUT -bbx x1 y1 x2 y2

Helper to extract roi from bounding box

optional arguments:
  -h, --help         show this help message and exit
  -il [IL [IL ...]]  Image products
  -out OUT           Extracts directory
  -bbx x1 y1 x2 y2   Bounding box from two points (x1, y1) and (x2, y2)

For example, if you have downloaded the maxar example data Maxar WorldView example files, you can choose a region of interest with geojson.io.

And then extract region, create config file and launch cars:

cars-extractroi -il *.NTF -out ext_dir -bbx -58.5896 -34.4872 -58.5818 -34.4943
cars-starter -il ext_dir/*.tif -out out_dir > config.json
cars config.json

Monitor tiles progression

cars-dashboard script allows to monitor the progression of tiles computation on a web browser.

usage: cars-dashboard [-h] -out OUT

Helper to monitor tiles progress

optional arguments:
-h, --help  show this help message and exit
-out OUT    CARS output folder to monitor

For example, if you want to monitor the computation of a CARS run:

cars-dashboard -out output_cars

Make a simple pan sharpening

In the case of Pleiades sensors, the XS color isn’t superimposable to the Panchromatic image.

It can be recommended to apply a P+XS pansharpening with OTB.

otbcli_BundleToPerfectSensor -inp image.tif -inxs color.tif -out color_pxs.tif

Make a water mask

To produce a water mask from R,G,B,NIR images, it can be recommended to compute a Normalized Difference Water Index (NDWI) and threshold the output to a low value.

The low NDWI values can be considered as water area.

gdal_calc.py -G input.tif --G_band=2 -N input.tif --N_band=4 --outfile=mask.tif --calc="((1.0*G-1.0*N)/(1.0*G+1.0*N))>0.3" --NoDataValue=0

See next section to apply a gdal_translate to convert the mask with 1bit image struture.

Convert image to binary image

To translate single image or multiband image with several nbits per band to 1bit per band, it can be recommended to use gdal_translate as follows:

gdal_translate -ot Byte -co NBITS=1 mask.tif mask_1nbit.tif

Add band name / description in tiff files metadata

To add a band name /description in tiff files, for classification or color files in order to be used:

data_in = gdal.Open(infile, gdal.GA_Update)
band_in = data_in.GetRasterBand(inband)
band_in.SetDescription(band_description)
data_in = None

Post process output

Merge Laz files

CARS generates several laz files corresponding to the tiles processed. Merge can be done with laszip.

To merge them:

laszip -i data\*.laz -merged -o merged.laz