A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

Daniel Clewley*, Peter Bunting, James Shepherd, Sam Gillingham, Neil Flood, John Dymond, Richard Lucas, John Armston, Mahta Moghaddam

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Citations (SciVal)
343 Downloads (Pure)

Abstract

A modular system for performing Geographic Object-Based Image Analysis (GEOBIA), using entirely open source (General Public License compatible) software, is presented based around representing objects as raster clumps and storing attributes as a raster attribute table (RAT). The system utilizes a number of libraries, developed by the authors: The Remote Sensing and GIS Library (RSGISLib), the Raster I/O Simplification (RIOS) Python Library, the KEA image format and TuiView image viewer. All libraries are accessed through Python, providing a common interface on which to build processing chains. Three examples are presented, to demonstrate the capabilities of the system: (1) classification of mangrove extent and change in French Guiana; (2) a generic scheme for the classification of the UN-FAO land cover classification system (LCCS) and their subsequent translation to habitat categories; and (3) a national-scale segmentation for Australia. The system presented provides similar functionality to existing GEOBIA packages, but is more flexible, due to its modular environment, capable of handling complex classification processes and applying them to larger datasets.

Original languageEnglish
Pages (from-to)6111-6135
Number of pages25
JournalRemote Sensing
Volume6
Issue number7
DOIs
Publication statusPublished - Jul 2014

Keywords

  • GEOBIA
  • open source
  • segmentation
  • Python
  • Raster Attribute Table
  • RAT
  • TuiView
  • RIOS
  • RSGISLib
  • GDAL
  • LAND-COVER CLASSIFICATION
  • SOFTWARE
  • 6S

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