modified object-oriented classification algorithm and its application in high-resolution remote-sensing imagery

• Published in: International journal of remote sensing Vol. 33; no. 10; pp. 3048 - 3062
• Main Authors: Chen, Zhong, Wang, Guoyou, Liu, Jianguo
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.01.2012
• Subjects: algorithms; Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; image analysis; Internal geophysics; remote sensing; spectral analysis; Teledetection and vegetation maps; texture; algorithms; Modification; textures; Extract; Image; Information; Earth; Semantics; imagery; segmentation; high resolution; object oriented; fuzzy logic; Average; Satellite observation; remote sensing; Representation; Method; classification; extraction; Weight; Previous study; Characteristics; geometry; Pixel
• ISSN: 1366-5901; 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2011.625055

High-resolution satellite images offer abundant information on the Earth's surface for remote-sensing applications. The traditional pixel-based image classification method only used by spectral information has been proved to have several drawbacks. To satisfactorily interpret high-resolution imagery, other important information such as geometry, texture and semantics must be used, which are represented not only in single pixels but in meaningful image objects. So, a modified high-resolution image classification algorithm with multi-characteristics based on objects is presented in this article. First, image objects are extracted by multi-scale multi-characteristic segmentation. Second, characteristics such as spectral information, geometry, texture and semantics are extracted by the corresponding extraction algorithm. Finally, the image objects are classified by means of fuzzy-logic classification with a weighted average calculation method. Preliminary results show promise in terms of classification quality and accuracy.