Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine

• Published in: International journal of remote sensing Vol. 31; no. 6; pp. 1453 - 1470
• Main Authors: Li, Haitao, Gu, Haiyan, Han, Yanshun, Yang, Jinghui
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Abingdon Taylor & Francis 01.02.2010
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Classification; Color; Colour; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Imagery; Internal geophysics; Object oriented; Object-oriented programming; Remote sensing; Support vector machines; Teledetection and vegetation maps; algorithms; color; data; geographic information systems; Support vector machine; Data mining; Information; Result; imagery; segmentation; Plant cover; Structure; Vector; land cover; high resolution; image processing; object oriented; remote sensing; utilization; Method; classification; Code; Advantage; digital images; Pixel
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160903475266

This paper presents a new object-oriented land cover classification method that integrates raster analysis and vector analysis. The method adopts an improved colour structure code (CSC) for segmentation and support vector machine (SVM) for classification using high resolution (HR) QuickBird data. It combines the advantages of digital image processing (efficient improved CSC segmentation), geographical information systems (GIS) (vector-based feature selection), and data mining (intelligent SVM classification) to interpret images from pixels to objects and thematic information. The improved CSC segmentation not only achieves robust and accurate results but also combines boundary information that the traditional CSC algorithm does not consider. The SVM used for classification has the advantages of solving sparse sampling, nonlinear, high-dimensional and global optimum problems, compared with other classifiers. The results demonstrate that the new object-oriented classification method significantly outperforms some other objected-oriented classification methods such as the objected-oriented method based on traditional CSC and SVM, and perfect classification results are obtained from the classification processing, including not only the classification method, but also preprocessing, sample selection and post-processing.