Name
Title | ||
|---|---|---|
Leveraging Machine Learning to Extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): A Case Study in Forest-Type Mapping. |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/rs11050503 | REMOTE SENSING |
Keywords | Field | DocType |
GEOBIA,rule-based classification,ontology,machine learning,random forests,rules extraction,variogram,semantic similarities,semantic variogram | Ontology,Variogram,Feature extraction,Sensor fusion,Lidar,Ranging,Artificial intelligence,Earth observation,Geology,Random forest,Machine learning | Journal |
Volume | Issue | Citations |
11 | 5 | 0 |
PageRank | References | Authors |
0.34 | 24 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sachit Rajbhandari | 1 | 59 | 4.95 |
| Jagannath Aryal | 2 | 55 | 12.31 |
| j e osborn | 3 | 17 | 2.32 |
| Arko Lucieer | 4 | 455 | 46.51 |
| Robert Musk | 5 | 0 | 0.34 |