Towards a framework for agent-based image analysis of remote-sensing data

Abstract

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).

Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data

“…Future investigations on defuzzification should also comprise defuzzification of intermediate fuzzy classification results and their reliability within rather complex analysis processes such as ABIA [25]. Especially in ABIA, quantified reliability, that is, a degree of reliability expressed by uncertainty, fuzziness and ambiguity, could be defined as a goal for agents to achieve in order to control autonomous adaptation processes.…”

“…In the context of Agent Based Image Analysis (ABIA), maximising the reliability of individual entities (aka image object agents), or the overall reliability of a fuzzy classification result could be defined as a goal for software agents, and therefore contribute to optimizing autonomously adapted rule sets or image objects [25].…”

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

“…Object-based feature extraction methods enable the clustering of several homogeneous pixels and the analysis of both local and global properties; moreover, the successful development of feature extraction technologies for HRS satellite imagery has greatly increased its usefulness in many remote sensing applications [10][11][12][13][14][15][16][17][18]. Blaschke et al [10] discussed several limitations of pixel-based methods in analyzing high-resolution images and crystallized core concepts of Geographic Object Based Image Analysis.…”

“…The similarity measure is calculated for each pair of neighboring regions, and the merging criterion is used to choose the neighboring pair of regions that are most similar. The weighted Euclidean distance (WED) is used to measure the similarity [17,18]. In the following, it is assumed that two neighboring regions, denoted by R 1 and R 2 , with region models M R 1 and M R 2 and region sizes of N R 1 and N R 2 pixels, respectively, are evaluated based on the dissimilarity measure d, which is denoted by d pR 1 , R 2 q.…”

Special issue: Remote sensing of our changing landscapes with Geographic Object-based Image Analysis (GEOBIA)

“…During this stage, the GEOBIA community has greatly extended the interest from land-use/land-cover mapping to many other fields, such as improving urban energy efficiency , capturing latent spatial phenomena under policy concern , and forest burn severity estimation . Accordingly, new GEOBIA algorithms were developed with emphases on analyzing novel data types (e.g., hyperspectral; Schäfer et al 2016), multi-source data integration (e.g., optical and LiDAR; Godwin, Chen, and Singh 2015), automation of scale determination (e.g., enhancing intra-segment homogeneity and inter-segment heterogeneity; Yang, He, and Weng 2015), semantic segmentation (e.g., employing Deep Convolutional Neural Networks (DCNN); Marmanis et al 2016), feature selection (e.g., utilizing machine learning; Ma et al 2017), automating the adaptation and adjustment of rule sets (e.g., agent-based image analysis; Hofmann et al 2015), ontology-driven modeling (e.g., Arvor et al 2013), etc. The maturity of GEOBIA foundations, frameworks, and software allowed researchers and practitioners to effectively analyze high-resolution imagery, while research findings further published in non-remote-sensing journals, such as Journal of Environmental Management, Landscape and Urban Planning, Ecological Informatics, Natural Hazards and Earth System Sciences, and Journal of Archaeological Science.…”