From pixels to grixels: a unified functional model for geographic-object-based image analysis

Lizarazo, Ivan and Elsner, Paul (2008) From pixels to grixels: a unified functional model for geographic-object-based image analysis. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38 (4/C1), ISSN 1682-1750.

Abstract

Geographic Object-Based Image Analysis (GEOBIA) aims to better exploit earth remotely sensed imagery by focusing on building image-objects resembling the real-world objects instead of using raw pixels as basis for classification. Due to the recentness of the field, concurrent and sometimes competing methods, terminology, and theoretical approaches are evolving. This risk of babelization has been identified as one of the central threats for GEOBIA, as it could hinder scientific discourse and the development of a generally accepted theoretical framework. This paper contributes to the definition of such ontology by proposing a general functional model of the remote sensing image analysis. The model compartmentalizes the remote sensing process into six stages: (i) sensing the earth surface in order to derive pixels which represent incomplete data about real-world objects; (ii) pre-processing the pixels in order to remove atmospheric, geometric, and radiometric distortions; (iii) grouping the pre-processed pixels (prixels) to produce image-objects (grouped pixels or grixels) at one or several scales; (iv) feature analysis to examine and measure relevant spectral, geometric and contextual properties and relationships of grixels in order to produce feature vectors (vexcels) and decision rules for subsequent discrimination; (v) assignation of grixels to pre-defined qualitative or quantitative land cover classes, thus producing pre-objects (preliminary objects); and (vi) post-processing to refine the previous results and output the geographic objects of interest. The grouping stage may be analized from two different perpectives: (i) discrete segmentation which produces well-defined image-objects, and (ii) continuous segmentation which produces image-fields with indeterminate boundaries. The proposed generic model is applied to analyze two specific GEOBIA software implementations. A functional decomposition of discrete segmentation is also discussed and tested. It is concluded that the proposed framework enhances the evaluation and comparison of different GEOBIA approaches and by this is helping to establish a generally accepted ontology.

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