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

Remote Sensing

Daniel Clewley³  Peter Bunting⁴  James Shepherd¹  Sam Gillingham¹  Neil Flood⁵  John Dymond⁶  Richard Lucas²  John Armston⁵ 

[1] Informatics Team, Landcare Research, Private Bag 11052, Palmerson North, New Zealand; E-Mails:;School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia; E-Mail:;Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA; E-Mail:;Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, Ceredigion, Wales, SY23 3DB, UK; E-Mail:;Remote Sensing Centre, Science Division, Department of Science, Information Technology, Innovation and the Arts, Brisbane, Queensland 4001, Australia; E-Mails:;Soils and Landscape Team, Landcare Research, Private Bag 11052, Palmerson North, New Zealand; E-Mail:

关键词: GEOBIA;    open source;    segmentation;    Python;    Raster Attribute Table;    RAT;    TuiView;    RIOS;    RSGISLib;    GDAL;   

DOI  :  10.3390/rs6076111

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland