ISSN 0016-7126 (Print)
ISSN 2587-8492 (Online)
Abstract:
The article deals with a key challenge in Geographic Object-Based Image Analysis (GEOBIA), lacking a unified approach to describing land cover classes, which leads to ambiguity and low reproducibility of results. To solve this problem, a unified framework for describing land cover classes was proposed; it provides a connection between the EAGLE semantic model and the interpretation features computed in the Trimble eCognition environment. The methodology is based on creating a two-component system: a registry of standard complex classes, ensuring compatibility with the core EAGLE model, and that of non-standard complex classes, which allows users to introduce more detailed, task-specific classes while maintaining a hierarchical relationship with the standard model. Furthermore, the authors present a comprehensive study of the eCognition features efficiency, aimed at reducing their number to be considered in the practical application of the methodology. As a result, a group of the 10 most informative features was identified, and a hierarchy to apply them was proposed. The findings enable avoiding a full-scale search of all available features, focusing on a limited but effective set instead, which significantly accelerates development of classification rules and improves the objectivity and reproducibility of the results
The research was granted by the Russian Science Foundation No. 25-27-00361, https://rscf.ru/project/25-27-00361/
References:
| 1. Uchaev D. V., Uchaev Dm. V. Razrabotka tekhnologii ob"ektno-orientirovannoi klassifikatsii tsvetnykh aerokosmicheskikh izobrazhenii, osnovannoi na ontologicheskom predstavlenii znanii. Izvestia vuzov. Geodesy and Aerophotosurveying, 2021, Vol. 65, no. 1, pp. 38–51. |
| 2. Arvor D., Durieux L., Andrés S., Laporte M.-А. (2013) Advances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, no. 82, pp. 125–137. DOI: 10.1016/j.isprsjprs.2013.05.003. |
| 3. Belgiu M., Drǎguţ L., Strobl J. (2014) Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery. ISPRS Journal of Photogrammetry and Remote Sensing, no. 87, pp. 205–215. DOI: 10.1016/j.isprsjprs.2013.11.007. |
| 4. Blaschke T., Hay G. J., Kelly M., Lang S., Hofmann P., Addink E., Feitosa R. Q., van der Meer F., van der Werff H., van Coillie F., Tiede D. (2014) Geographic object-based image analysis – towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, no. 87, pp. 180–191. DOI: 10.1016/j.isprsjprs.2013.09.014. |
| 5. Cuypers S., Nascetti A., Vergauwen M. (2023) Land use and land cover mapping with VHR and multi-temporal Sentinel-2 imagery. Remote Sensing, no. 15 (10), DOI: 10.3390/rs15102501. |
| 6. Duro D. C., Franklin S. E., Dubé M. G. (2012) Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests. International Journal of Remote Sensing. Taylor and Francis, no. 33 (14), pp. 4502–4526. DOI: 10.1080/01431161.2011.649864. |
| 7. Le Hegarat-Mascle S., Vidal-Madjar D., Dinstein I. (1973) Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, no. 3, pp. 610–621. DOI: 10.1109/TSMC.1973.4309314. |
| 8. Hay G. J., Castilla G. (2008) Geographic object-based image analysis (GEOBIA): A new name for a new discipline. Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications. Springer, Berlin, pp. 75–89. DOI: 10.1007/978-3-540-77058-9_4. |
| 9. He T., Chen J., Pan D. (2025) GOFENet: A hybrid transformer–cnn network integrating GEOBIA-based object priors for semantic segmentation of remote sensing images. Remote Sensing, no. 17 (15), DOI: 10.3390/rs17152652. |
| 10. Kim M., Madden M., Warner T. A. (2009) Forest type mapping using object-specific texture measures from multispectral Ikonos imagery. Photogrammetric Engineering and Remote Sensing, no. 75 (7), pp. 819–829. DOI: 10.14358/PERS.75.7.819. |
| 11. Kutz K., Cook Z., Linderman M. (2022) Object based classification of a riparian environment using ultra-high resolution imagery, hierarchical landcover structures, and image texture. Scientific Reports, no. 12 (1), DOI: 10.1038/s41598-022-14757-y. |
| 12. Laliberte A. S., Browning D. M., Rango A. (2012) A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery. International Journal of Applied Earth Observation and Geoinformation, no. 15, pp. 70–78. DOI: 10.1016/j.jag.2011.05.011. |
| 13. Mora D., León-Sánchez C., Lizarazo I. (2022) Optimization of urban land-cover classification workflow based on geographic-object analysis using very-high-resolution imagery. DYNA, no. 89 (220), pp. 43–53. |
| 14. Morin E., Razafimbelo N. T., Yengué J.-L., Guinard Y., Grandjean F., Bech N. (2024) Mapping past land cover on Poitiers in 1993 at very high resolution using GEOBIA approach and open data. Data in Brief, Volume 52, no. 109829, DOI: 10.1016/j.dib.2023.109829. |
| 15. Shi L., Wan Y., Gao X., Wang M. (2018) Feature selection for object-based classification of high-resolution remote sensing images based on the combination of a genetic algorithm and tabu search. Computational intelligence and neuroscience, no. 6595792, DOI: 10.1155/2018/6595792. |
Citation:
| (2025) Developing a unified framework for describing classes of land cover components in object-based classification of aerospace imagery. Geodesy and cartography = Geodeziya i Kartografiya, 86(12), pp. 46-55. (In Russian). DOI: 10.22389/0016-7126-2025-1026-12-46-55 |