Leveraging Machine Learning to Extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): A Case Study in Forest-Type Mapping

Autor:	Sachit Rajbhandari, Arko Lucieer, Jagannath Aryal, Jon Osborn, RA Musk
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	random forests 010504 meteorology & atmospheric sciences Computer science Science Feature extraction 0211 other engineering and technologies semantic similarities 02 engineering and technology Machine learning computer.software_genre 01 natural sciences semantic variogram ontology Variogram 021101 geological & geomatics engineering 0105 earth and related environmental sciences GEOBIA business.industry variogram Object (computer science) Sensor fusion Random forest Identification (information) Lidar machine learning Feature (computer vision) rule-based classification General Earth and Planetary Sciences Artificial intelligence business computer rules extraction
Zdroj:	Remote Sensing Volume 11 Issue 5 Pages: 503 Remote Sensing, Vol 11, Iss 5, p 503 (2019)
ISSN:	2072-4292
DOI:	10.3390/rs11050503
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da2ab2bb3d4afedf0142030fb8a4f6ac Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.