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Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities
Open accessAbstract
Over the last two decades (since ca. 2000), Geographic Object-Based Image Analysis (GEOBIA) has emerged as a new paradigm to analyzing high-spatial resolution remote-sensing imagery. During this time, research interests have demonstrated a shift from the development of GEOBIA theoretical foundations to advanced geo-object-based models and their implementation in a wide variety of real-world applications. We suggest that such a rapid GEOBIA evolution warrants the need for a systematic review that defines the recent developments in this field. Therefore, the main objective of this paper is to elucidate the emerging trends in GEOBIA and discuss potential opportunities for future development. The emerging trends were found in multiple subfields of GEOBIA, including data sources, image segmentation, object-based feature extraction, and geo-object-based modeling frameworks. It is our view that understanding the state-of-the-art in GEOBIA will further facilitate and support the study of geographic entities and phenomena at multiple scales with effective incorporation of semantics, informing high-quality project design, and improving geo-object-based model performance and results.
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Cited by
Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning
Kemker, R., Salvaggio, C., & Kanan, C. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.014 · Cited by 558
Liu, T., & Abd-Elrahman, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.03.006 · Cited by 103
Metadata
- Authors
- Chen, G., Weng, Q., Hay, G. J., & He, Y.
- Venue
- GIScience & Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2018-01-18
- Volume
- 55
- Issue
- 2
- Pages
- 159-182
- ISSN
- 1548-1603 (ISSN-L)
- 1943-7226
- OpenAlex
- W2782934949
- Cited by
- 287
- References
- 2
- FWCI
- 28.39
- Type
- journal article
- Language
- en
- Open access
- bronze
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