One-Class Classification for Mapping a Specific Land-Cover Class: SVDD Classification of Fenland

• Published in: IEEE transactions on geoscience and remote sensing Vol. 45; no. 4; pp. 1061 - 1073
• Main Authors: Sanchez-Hernandez, C., Boyd, D.S., Foody, G.M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2007; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Applied geophysics; Classification; Classifiers; Conservation; Earth sciences; Earth, ocean, space; Exact sciences and technology; fen; Habitats; Heart; Image classification; Image converters; Imagery; Information analysis; Information resources; Internal geophysics; l and cover; Land cover; Mapping; Remote monitoring; Remote sensing; Satellites; support vector; Technological innovation; Training; Training data; Thematic Mapper; habitat; maps; image classification; Space remote sensing; Landsat; accuracy; classification; fen; support vector; conservation; imagery; maximum likelihood; land cover
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2006.890414

Remote sensing is a major source of land-cover information. Commonly, interest focuses on a single land-cover class. Although a conventional multiclass classifier may be used to provide a map depicting the class of interest, the analysis is not focused on that class and may be suboptimal in terms of the accuracy of its classification. With a conventional classifier, considerable effort is directed on the classes that are not of interest. Here, it is suggested that a one-class-classification approach could be appropriate when interest focuses on a specific class. This is illustrated with the classification of fenland, a habitat of considerable conservation value, from Landsat Enhanced Thematic Mapper Plus imagery. A range of one-class classifiers is evaluated, but attention focuses on the support-vector data description (SVDD). The SVDD was used to classify fenland with an accuracy of 97.5% and 93.6% from the user's and producer's perspectives, respectively. This classification was trained upon only the fenland class and was substantially more accurate in fen classification than a conventional multiclass maximum-likelihood classification provided with the same amount of training data, which classified fen with an accuracy of 90.0% and 72.0% from the user's and producer's perspectives, respectively. The results highlight the ability to classify a single class using only training data for that class. With a one-class classification, the analysis focuses tightly on the class of interest, with resources and effort not directed on other classes, and there are opportunities to derive highly accurate classifications from small training sets