Category Archives: Uncategorized

Introduction to RSGISLib Training Course

A course introducing RSGISLib was recently given in Japan by Pete Bunting. Material from the course is available to download from the following links:

The course covers pre-processing of PALSAR SAR data followed by a demonstration of pixel-based and object-based classification. All the required data and scripts are included in the package.

If you have any questions on the course please use the RSGISLib support Google group:!forum/rsgislib-support

Convert LaTeX to Word

A big problem with writing in LaTeX is collaborating with colleagues who don’t use it. One option is to generate a Word .docx version and use the comments and track changes features in Word / LibreOffice. This does require manually copying the changes back to LaTeX so isn’t quite as nice as using latexdiff (see earlier post) but is slightly easier than adding comments to a PDF.

The best program I’ve found for converting LaTeX to Word is the open source (GPL) command line tool, pandoc (

Basic usage is quite straightforward:

pandoc latex_document.tex -o latex_document_word_version.docx

The conversion isn’t perfect, figures and tables can get a bit mangled, but it does a good job with the text.

Pandoc can convert between many different formats, including from markdown and reStructuredText (commonly used for software documentation) so it is worth having installed.

Create a CSV with the coordinates from geotagged photos

Recently I took a lot of photos on my phone during fieldwork (survey for the NERC-ARF LiDAR and hyperspectral calibration flights) and wanted to extract the GPS coordinates from each photo so I could load them into QGIS using the add delimited text dialogue. The aim wasn’t to have precise locations for each photo (GPS positions for each of the points surveyed were recorded separately) but to give a quick idea of the locations we’d visited before the main GPS data were processed.

I while ago (2012!) I wrote a script for this task. The script (CreateJPEGKMZ) requires pillow and the imagemagick command line tools.

It code isn’t particularly tidy (despite my recent updates) but it basically pulls out the GPS location from the EXIF tags and writes this to a CSV file. To create a KMZ a thumbnail image is created and a corresponding KML file. Both the thumbnail and KML are then zipped together to generate the KMZ which can be opened in GoogleEarth.

To use the script to write a CSV file: --outcsv gps_points.csv input_jpeg_files

To also create KMZ files for each photo: --outcsv gps_points.csv \
                 --outkmz output_kmz_files 

This was also a good lesson in the benefits of having scripts in version control and publicly available.

Convert EASE-2 grid cell to latitude and longitude using Python

The EASE-2 grid is used for a number of NASA datasets including SMAP. It is described in the following paper:

Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., & Savoie, M. H. (2012). EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets. ISPRS International Journal of Geo-Information, 1(3), 32–45.

Files with the centre coordinate of each cell for EASE-2 grids at different resolutions are available from as well as tools for conversion.

To read these files into Python the following steps can be used:

  1. Download the relevant gridloc file.

    The FTP link for the grid location files is:

    For this example I’ve chosen the 36km cylindrical EASE-2 grid (gridloc.EASE2_M36km.tgz)

  2. Un-tar using:
    tar -xvf gridloc.EASE2_M36km.tgz
  3. Read the files into Python:
    import numpy
    # Read binary files and reshape to correct size
    # The number of rows and columns are in the file name
    lats = numpy.fromfile('EASE2_M36km.lats.964x406x1.double', 
    lons = numpy.fromfile('EASE2_M36km.lons.964x406x1.double', 
    # Extract latitude and longitude
    # for a given row and column 
    grid_row = 46
    grid_column = 470
    lat_val = lats[grid_row, grid_column]
    lon_val = lons[grid_row, grid_column]

Add an ENVI header to JAXA Global Mangrove Watch PALSAR tiles

This is an update to a script I wrote a while ago for working with PALSAR tiles from JAXA’s Global Mangrove Watch.

Untar files
Using GNU parallel [1] you can untar a directory of files at once:

ls *tar.gz | parallel -j 2 tar -xf 

Which will untar two files at a time (depending on how fast your disk is you may wish to set more running at once).

Add ENVI format header
Adding an ENVI header to each of the files (using information from the JAXA header file) allows the files to be easily read using GDAL. Following a similar method outlined in previous posts the script to add ENVI headers can be downloaded from

It is run using:

python KC_*

This will add a header for all files in each directory matching ‘KC*’

Create mosaic using RSGISLib

Using the ‘’ utilities from RSGISLib, a mosaic can be created for each dataset using: -i . -s '*HH' -o palsar_hh.kea -ot UInt16 -i . -s '*HV' -o palsar_hv.kea -ot UInt16 -i . -s '*_linci' -o palsar_linci.kea -ot Byte -i . -s '*_mask' -o palsar_mask.kea -ot Byte -i . -s '*_date' -o palsar_date.kea -ot UInt16

A similar procedure should work for other datasets by modifying the Python script used to produce the ENVI header files.

[1] O. Tange (2011): GNU Parallel – The Command-Line Power Tool, ;login: The USENIX Magazine, February 2011:42-47.

Tracking changes in a LaTeX document

One of the problems people often have with using LaTeX for collaborative writing is that it is difficult to track changes in a document, like in Word.

As .tex files are text documents version control such as Git or Mercurial can be used to keep track of changes in the LaTeX source. However, looking at differences in .tex files is not as easy as having a formatted copy of the document with the changes marked, in particular for people not used to LaTeX.

The latexdiff command can be used to create a LaTeX document with the changes marked, from which a PDF can be created showing where the text has changed. For example:


Basic usage is:

latexdiff origionaldoc.tex changeddoc.tex > changes.tex

By default the command will print everything to the terminal so the output needs to be redirected (using >) to a file.

When writing papers we commonly have a shell script to generate a change tex file, create a PDF and remove temp files.

# Create diff file
latexdiff --exclude-textcmd "section,subsection,sub subsection" \
   201403_GOBIA_RSGISLib_RIOS_origional.tex \
   201403_GOBIA_RSGISLib_RIOS.tex > \

# Create PDF
pdflatex 201403_GOBIA_RSGISLib_RIOS_changes_temp.tex
bibtex 201403_GOBIA_RSGISLib_RIOS_changes_temp
pdflatex 201403_GOBIA_RSGISLib_RIOS_changes_temp.tex
pdflatex 201403_GOBIA_RSGISLib_RIOS_changes_temp.tex

# Rename PDF and move extra files.
mv 201403_GOBIA_RSGISLib_RIOS_changes_temp.pdf 201403_GOBIA_RSGISLib_RIOS_changes.pdf
rm 201403_GOBIA_RSGISLib_RIOS_changes_temp.*

Note sometimes changes for particular tex commands can cause problems when creating a PDF, producing an error along these lines:

! Argument of \UL@word has an extra }.
    <inserted text>
l.292 ...\DIFaddbegin \DIFadd{Methods}\DIFaddend }

To stop this happening you can exclude particular commands (e.g., section) using the ‘–exclude-textcmd’ flag. (Thanks to this post on stack exchange for the tip).

Reading KEA files in MATLAB

The KEA file format is built on HDF5 so anything which can read HDF5 can read a KEA file, including MATLAB. I’m not quite sure why you’d want to use MATLAB instead of using Python, in particular as you can read KEA files and many other formats through GDAL. However, I’ve been claiming it’s possible to use KEA with MATLAB for a while so I though it would be a good idea to post an example!

This example uses CASI data over Injune, which is part of the test dataset for RSGISLib, you can download from here.

1. Get file information

info = h5info('injune_p142_casi_sub_ll.kea');
% Get bandnames

2. Read in spatial information
If you don’t care about the geospatial information you can skip this step.

% Read in resolution
res = h5read('injune_p142_casi_sub_ll.kea','/HEADER/RES');
xRes = res(1);
yRes = res(2); 

% Top left coordinate
topLeft = h5read('injune_p142_casi_sub_ll.kea','/HEADER/TL');
topLeftX = topLeft(1); % Longitude
topLeftY = topLeft(2); % Latitude

% Size
imageSize = h5read('injune_p142_casi_sub_ll.kea','/HEADER/SIZE');
imageSizeX = imageSize(1);
imageSizeY = imageSize(2);

% Calculate bottom right
bottomRightX = topLeftX + (double(imageSizeX) * xRes);
bottomRightY = topLeftY + (double(imageSizeY) * yRes);

lon_axis = linspace(topLeftX,bottomRightX,imageSizeX);
lat_axis = linspace(bottomRightY,topLeftY,imageSizeY);

3. Read in data
This assumes reading in bands 12, 8 and 3.

band12 = transpose(h5read('injune_p142_casi_sub_ll.kea','/BAND12/DATA'));
band8 = transpose(h5read('injune_p142_casi_sub_ll.kea','/BAND8/DATA'));
band3 = transpose(h5read('injune_p142_casi_sub_ll.kea','/BAND3/DATA'));

4. Display data
A three band composite is created, this is stretched and then displayed.

% Create composite
composite = cat(3,band12,band8,band3);

% Apply linear stretch
stretchedComposite = imadjust(composite,stretchlim(composite));

% View image
h = imagesc(lon_axis, lat_axis, stretchedComposite);

If you’ve used the image from the example the output should look something like this:


More details about the KEA format (including a description of the data structure) are available in the following paper: Peter Bunting and Sam Gillingham, The KEA image file format, Computers & Geosciences, Volume 57, August 2013, Pages 54-58, ISSN 0098-3004,