Abstract
Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy.
Highlights
Geographic Object-Based Image Analysis (GEOBIA) is a widely used and still developing new approach to image segmentation and classification [1]
Semantic similarity methods were used to characterise the similarities between different classes and we identified similarity measures that are appropriate for an O-GEOBIA framework
This study proposes an extension of an O-GEOBIA framework [6] that uses Machine learning (ML) techniques method for automatic generation of rules
Summary
Geographic Object-Based Image Analysis (GEOBIA) is a widely used and still developing new approach to image segmentation and classification [1]. The aim of any GEOBIA application is to translate expert knowledge associated with real-world features into the GEOBIA process [1,3,4] in a manner that is formal, objective and transferable. One approach to this challenge is to employ an ontology to formally capture and represent the expert knowledge [4,5], referred to here as Ontology-driven GEOBIA (O-GEOBIA). Low-level information extracted from sensor data in combination with high-level information from domain knowledge provides a basis for creating rule sets for a rule-based classifier
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