Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects’ properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).

Object Based Image Analysis (OBIA) has meanwhile been established as a paradigm for analyzing remotely sensed image data. Although the degree of automation for OBIA methods has increased for several applications, especially in the domain of remote sensing, robust and transferable object-based solutions for automated image analysis of sets of images or even large image archives are still rare. One of the reasons for this lack of robustness and transferability is the high complexity of remote sensing image contents: Especially in Very High Resolution (VHR) remote sensing data, under varying imaging conditions or sensor characteristics, the objects' properties can vary unpredictably. Although earlier work has demonstrated that OBIA rule sets bear a high potential of transferability these rule sets need to be adapted manually in order to receive acceptable results, or the classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering and robotics. The aims of such integration are a) rule sets which can be adapted autonomously according to varying imaging data, and b) image objects which can adapt and adjust themselves in order to best possibly represent the objects of interest in an image. This paper briefly introduces a framework for Agent Based Image Analysis (ABIA) and presents our first research results.