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

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.

Keywords

• GEOBIA
• open source
• segmentation
• Python
• Raster Attribute Table
• RAT
• TuiView
• RIOS
• RSGISLib
• GDAL