Automatic habitat classification using image analysis and random forest

Torres, Mercedes; Qiu, Guoping

Abstract

Habitat classification is important for monitoring the environment and biodiversity. Currently, this is done manually by human surveyors, a laborious, expensive and subjective process. We have developed a new computer habitat classification method based on automatically tagging geo-referenced ground photographs. In this paper, we present a geo-referenced habitat image database containing over 1000 high-resolution ground photographs that have been manually annotated by experts based on a hierarchical habitat classification scheme widely used by ecologists. This is the first publicly available image database specifically designed for the development of multimedia analysis techniques for ecological (habitat classification) applications. We formulate photograph-based habitat classification as an automatic image tagging problem and we have developed a novel random forest based method for annotating an image with the habitat categories it contains. We have also developed an efficient and fast random-projection based technique for constructing the random forest. We present experimental results to show that ground-taken photographs are a potential source of information that can be exploited in automatic habitat classification and that our approach is able to classify with a reasonable degree of confidence four of the main habitat classes: Woodland and Scrub, Grassland and Marsh, Heathland and Miscellaneous.

Citation

Torres, M., & Qiu, G. (2014). Automatic habitat classification using image analysis and random forest. Ecological Informatics, 23, 126-136. https://doi.org/10.1016/j.ecoinf.2013.08.002

• Journal Article Type: Article
• Acceptance Date: Aug 19, 2013
• Online Publication Date: Sep 2, 2013
• Publication Date: Sep 1, 2014
• Deposit Date: Sep 15, 2017
• Print ISSN: 1574-9541
• Electronic ISSN: 1574-9541
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Pages: 126-136
• DOI: https://doi.org/10.1016/j.ecoinf.2013.08.002
• Public URL: https://nottingham-repository.worktribe.com/output/1123815
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1574954113000733?via%3Dihub
• Related Public URLs: http://www.sciencedirect.com/science/article/pii/S1574954113000733