Abstract
This paper presents an unsupervised segmentation method based on the feature decomposition used for extracting residential areas in high resolution remote sensing image. The method was realized by multi-scale feature analysis with wavelet decomposition, constituting the feature space from differences of the internal, external structure in residential areas and the average spectral radiant intensity, making a self-adaptive segmentation by the constraint mean shift algorithm to texture features, and achieve the automatic extraction of residential areas. Experiment results show that the proposed method can eliminate the influence from over-detailed images and other factors caused by high resolution on the extraction of residential areas, and thus extract residential areas more effectively.
Original language | English |
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Pages (from-to) | 831-837 |
Number of pages | 7 |
Journal | Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University |
Volume | 39 |
Issue number | 7 |
DOIs | |
Publication status | Published - 1 Jan 2014 |
Externally published | Yes |
Keywords
- Constraint mean shift
- Image segmentation
- Unsupervised segmentation
- Wavelet decomposion
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Earth-Surface Processes