The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information
Open accessAbstract
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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Cites
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
A theory for multiresolution signal decomposition: the wavelet representation
Mallat, S. G. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.192463 · Cited by 21,372
Local Indicators of Spatial Association—LISA
Anselin, L. (1995) · Geographical Analysis · doi:10.1111/j.1538-4632.1995.tb00338.x · Cited by 12,919
A Computer Movie Simulating Urban Growth in the Detroit Region
Tobler, W. R. (1970) · Economic Geography · doi:10.2307/143141 · Cited by 9,719
Photogrammetric Engineering and Remote Sensing
Nicol, J., King, B., Quattrochi, D., Dowman, I. J., Ehlers, M., & Xiaoli, D. (2007) · UCL Discovery (University College London) · Cited by 9,325
Statistical and structural approaches to texture
Haralick, R. M. (1979) · Proceedings of the IEEE · doi:10.1109/proc.1979.11328 · Cited by 5,759
Object based image analysis for remote sensing
Blaschke, T. (2010) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2009.06.004 · Cited by 4,482
Remote Sensing And Image Interpretation
LILLESAND, T. M., Kiefer, R. W., & Chipman, J. (1979) · HAL · Cited by 4,463
Geographically Weighted Regression: The Analysis of Spatially Varying Relationships
Fotheringham, A. S., Brunsdon, C., & Charlton, M. E. (2002) · doi:10.25405/ncl.34562610 · Cited by 3,938
Support vector machines in remote sensing: a review
Mountrakis, G., Im, J., & Ogole, C. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.11.001 · Cited by 3,473
Cited by
The Future of Complementarity: Disentangling Causes from Consequences
Barry, K. E., Mommer, L., van Ruijven, J., Wirth, C., Wright, A. J., Bai, Y., Connolly, J., De Deyn, G. B., de Kroon, H., Isbell, F., et al. (2019) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2018.10.013 · Cited by 459
Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning
Nguyen, C., Sagan, V., Maimaitiyiming, M., Maimaitijiang, M., Bhadra, S., & Kwasniewski, M. T. (2021) · Sensors · doi:10.3390/s21030742 · Cited by 281
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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- Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information. European Journal of Remote Sensing. https://doi.org/10.5721/eujrs20144723
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
