Key to the successful application of remotely sensed data to real world problems is software that is capable of performing commonly used functions efficiently over large datasets, whilst being adaptable to new techniques. RSGISLib is an open source software library that was developed through research undertaken at Aberystwyth University for environmental remote sensing, particularly in relation to vegetation science. The software was designed to fill the gaps within existing software packages and to provide a platform to ease the implementation of new and innovative algorithms and data processing techniques. Users interact with the software through a python script, functions are used to parameterise the available commands, which have now grown to more than 300. A key feature of the python interface is that command options are easily recognisable to the user because of their logical and descriptive names. Through the python interface, processing chains and batch processing are supported. The software has been released under a GPL3 license and makes use of a number of other open source software libraries (e.g., GDAL/OGR), a user guide and the source code are available at http://www.rsgislib.org. When combined with other tools and software created in Aberystwyth, collaborates and the wider community a whole system for undertaking remote sensing data analysis. The system is used by many companies, government organisations and academics worldwide.
The are many examples of the system in use on our blog at https://spectraldifferences.wordpress.com.
Clewley, D.; Bunting, P.; Shepherd, J.; Gillingham, S.; Flood, N.; Dymond, J.; Lucas, R.; Armston, J.; Moghaddam, M. A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables. Remote Sensing 2014, 6, 6111-6135.
Bunting, P., Clewley, D., Lucas, R. M., & Gillingham, S. (2014). The Remote Sensing and GIS Software Library (RSGISLib). Computers and Geosciences, 62, 216–226. http://doi.org/10.1016/j.cageo.2013.08.007
Bunting, P., & Gillingham, S. (2013). The KEA image file format. Computers and Geosciences, 57, 54–58.
CONTACT: Pete Bunting