Geographic Object-Based Image Analysis - Towards a new paradigm

Abstract

The amount of scientific literature on (Geographic) Object-based Image Analysis - GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the 'per-pixel paradigm' and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.

Keywords: GEOBIA; GIScience; Image classification; Image segmentation; OBIA; Remote sensing.

Fig. 1

Subsets of Landsat TM scenes from Alaska (left) and Bangladesh (right). The left water filled channel intermingles with an old sediment-filled channel. The right portion of the water filled channel is overgrown by vegetation.

Fig. 2

False-colour digital image of a forest stand with sudden oak death in CA showing selected objects representing dead trees (grey) and associated hosts (magenta), and illustrating three common image spatial resolutions: 30 m, 4 m and 1 m. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).

Fig. 3

Idealized GEOBIA workflow that illustrates the iterative nature of the object building and classification process which incorporates GIScience concepts.

Fig. 4

Pixels and image-objects as information carriers: constant size, constant shape and implicit location vs. unique area/outline information derivatives and statistical descriptors of the interior. For the sake of simplicity, the temporal dimension is left out here.

Fig. 5

Hierarchy of image objects. Objects have (topological) neighbourhood relationships and have hierarchical relationships, such as “is-part-of” or “consists-of”. Respectively they can be (nearly) decomposed.

Fig. 6

Conceptual illustration of a multi-scale representation of an imaged landscape according to hierarchy theory principles. Object generation on the scale level of concern (‘focal level’) is embedded in higher level objects and lower level ones. Note that within GEOBIA, higher hierarchical levels usually correspond to increasing average object size. Objects have both self-integrative (‘part-of …’) and self-assertive (‘aggregates of …’) tendencies, and thereby feature the basic characteristics of holons. From Lang et al. (2004).

Fig. 7

Principle of the iterative workflow in GEOBIA: Initially generated image-objects are classified and enhanced iteratively step-by-step by incorporating procedural and object-domain knowledge described in an ontology and expressed and applied in a rule set.