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A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information
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This paper reviewed major remote sensing image classification techniques, including pixel-wise, sub-pixel-wise, and object-based image classification methods, and highlighted the importance of incorporating spatio-contextual information in remote sensing image classification. Further, this paper grouped spatio-contextual analysis techniques into three major categories, including 1) texture extraction, 2) Markov random fields (MRFs) modeling, and 3) image segmentation and object-based image analysis. Finally, this paper argued the necessity of developing geographic information analysis models for spatial-contextual classifications using two case studies.
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Metadata
- Authors
- Li, M., Zang, S., Zhang, B., Li, S., & Wu, C.
- Venue
- European Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2014-01
- Volume
- 47
- Issue
- 1
- Pages
- 389-411
- ISSN
- 2279-7254 (ISSN-L)
- OpenAlex
- W2111256709
- Cited by
- 538
- References
- 80
- FWCI
- 31.69
- Type
- journal article
- Language
- en
- Open access
- gold
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