期刊
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
卷 20, 期 -, 页码 77-85出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jag.2011.10.013
关键词
Change detection; Support vector machines; Graylevel co-occurrence matrix; Mathematical morphology; Very high resolution
资金
- Swiss National Science Foundation [200021-126505, PBLAP2-127713, PZ00P2-136827]
- Swiss National Science Foundation (SNF) [200021_126505, PBLAP2-127713] Funding Source: Swiss National Science Foundation (SNF)
In this paper we study an effective solution to deal with supervised change detection in very high geometrical resolution (VHR) images. High within-class variance as well as low between-class variance that characterize this kind of imagery make the detection and classification of ground cover transitions a difficult task. In order to achieve high detection accuracy, we propose the inclusion of spatial and contextual information issued from local textural statistics and mathematical morphology. To perform change detection, two architectures, initially developed for medium resolution images, are adapted for VHR: Direct Multi-date Classification and Difference Image Analysis. To cope with the high intra-class variability, we adopted a nonlinear classifier: the Support Vector Machines (SVM). The proposed approaches are successfully evaluated on two series of pansharpened QuickBird images. (C) 2011 Elsevier B.V. All rights reserved.
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