|
Enhancing Hyperspectral Image Classification Accuracy Using the Artificial Bee Colony Algorithm
|
Ali Asghar Torahi *   |
|
|
|
Abstract: (97 Views) |
Hyperspectral imaging, due to its capability in accurate material discrimination, is considered a powerful tool in Earth sciences and remote sensing. However, the inherent challenge of mixed pixels—arising from the limited spatial resolution of sensors—significantly reduces the accuracy of conventional classification methods. The main objective of this study is to propose and evaluate a novel swarm intelligence–based approach to improve spectral unmixing and consequently enhance classification accuracy. In this context, the performance of the Artificial Bee Colony (ABC) algorithm, as a global optimization method that does not rely on the assumption of pure pixels in the image, was compared with three conventional geometric algorithms: Pixel Purity Index (PPI), N-Finder, and Sequential Maximum Angle Convex Cone (SMACC). The research methodology was implemented on the standard AVIRIS dataset of the Cuprite region, Nevada. After applying essential preprocessing steps, including atmospheric correction (FLAASH) and dimensionality reduction (MNF), the endmembers of key minerals (Alunite, Kaolinite, and Chalcedony) were extracted using all four algorithms. The quality of the extracted endmembers was evaluated through quantitative metrics such as Spectral Angle Mapper (SAM), Euclidean Distance (ED), and Root Mean Square Error (RMSE), in comparison with the USGS spectral library. Finally, image classification was carried out using the SAM algorithm, and classification accuracy was assessed with Overall Accuracy (OA) and the Kappa coefficient. The key findings highlight the superior performance of the ABC algorithm, which achieved an overall classification accuracy of 91.14% and a Kappa coefficient of 78.42%, while the best result among geometric methods (PPI) recorded 86.19% OA and 62.69% Kappa. These results confirm the research hypothesis and demonstrate the high reliability of the ABC algorithm for precise hyperspectral image analysis in real-world environments lacking pure pixels. |
|
| Keywords: Hyperspectral Imaging, Endmember Extraction, Artificial Bee Colony, Spectral Unmixing, Classification Accuracy, AVIRIS Cuprite |
|
|
Full-Text [PDF 821 kb]
(20 Downloads)
|
Type of Study: Research |
Subject:
Photo&RS
Received: 2025/09/23 | Accepted: 2026/06/4
|
|
|
|
|
|
|
| References |
1. Alshahrani, A. A., Bchir, O., & Ben Ismail, M. M. (2025). Autoencoder-Based Hyperspectral Unmixing with Simultaneous Number-of-Endmembers Estimation. Sensors, 25(8), 2592. [ DOI:10.3390/s25082592] 2. Shaw, G. A., & Burke, H. K. (2003). Spectral imaging for remote sensing. Lincoln laboratory journal, 14(1), 3-28. 3. Gomez, R. B. (2002). Hyperspectral imaging: a useful technology for transportation analysis. Optical Engineering, 41(9), 2137-2143. [ DOI:10.1117/1.1497985] 4. Shivakanth, G., & Tanwar, P. S. (2018). Review on conventional and advanced classification approaches in remote sensing image processing. Int. J. Comput. Sci. Eng, 6, 871-879. [ DOI:10.26438/ijcse/v6i11.871879] 5. Foody, G. M. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote sensing of environment, 239, 111630. [ DOI:10.1016/j.rse.2019.111630] 6. Chang, C. I. (2003). Hyperspectral imaging: techniques for spectral detection and classification (Vol. 1). Springer Science & Business Media. 7. Song, W., Zhang, X., Yang, G., Chen, Y., Wang, L., & Xu, H. (2024). A study on dimensionality reduction and parameters for hyperspectral imagery based on manifold learning. Sensors, 24(7), 2089. [ DOI:10.3390/s24072089] 8. Keshava, N., & Mustard, J. F. (2002). Spectral unmixing. IEEE signal processing magazine, 19(1), 44-57. [ DOI:10.1109/79.974727] 9. Boardman, J. W. (1994, August). Geometric mixture analysis of imaging spectrometry data. In Proceedings of IGARSS'94-1994 IEEE International Geoscience and Remote Sensing Symposium (Vol. 4, pp. 2369-2371). IEEE. [ DOI:10.1109/IGARSS.1994.399740] 10. Plaza, J., Hendrix, E. M., García, I., Martín, G., & Plaza, A. (2012). On endmember identification in hyperspectral images without pure pixels: A comparison of algorithms. Journal of Mathematical Imaging and Vision, 42(2), 163-175. [ DOI:10.1007/s10851-011-0276-0] 11. Sun, X., Yang, L., Zhang, B., Gao, L., & Gao, J. (2015). An endmember extraction method based on artificial bee colony algorithms for hyperspectral remote sensing images. Remote Sensing, 7(12), 16363-16383. [ DOI:10.3390/rs71215834] 12. Rezaei, Y., Mobasheri, M. R., Zoej, M. V., & Schaepman, M. E. (2011). Endmember extraction using a combination of orthogonal projection and genetic algorithm. IEEE Geoscience and Remote Sensing Letters, 9(2), 161-165. [ DOI:10.1109/LGRS.2011.2162936] 13. Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., & Chanussot, J. (2012). Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches. IEEE journal of selected topics in applied earth observations and remote sensing, 5(2), 354-379. [ DOI:10.1109/JSTARS.2012.2194696] 14. Bioucas-Dias, J. M., & Nascimento, J. M. (2008). Hyperspectral subspace identification. IEEE Transactions on Geoscience and Remote Sensing, 46(8), 2435-2445. [ DOI:10.1109/TGRS.2008.918089] 15. Varshney, P. K., & Arora, M. K. (2004). Advanced image processing techniques for remotely sensed hyperspectral data. Springer Science & Business Media. [ DOI:10.1007/978-3-662-05605-9] 16. Nascimento, J. M., & Bioucas-Dias, J. M. (2009, September). Nonlinear mixture model for hyperspectral unmixing. In Image and Signal Processing for Remote Sensing XV (Vol. 7477, pp. 157-164). SPIE. [ DOI:10.1117/12.830492] 17. Winter, M. E. (1999, October). N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data. In Imaging spectrometry V (Vol. 3753, pp. 266-275). SPIE. [ DOI:10.1117/12.366289] 18. Gruninger, J. H., Ratkowski, A. J., & Hoke, M. L. (2004, August). The sequential maximum angle convex cone (SMACC) endmember model. In Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery X (Vol. 5425, pp. 1-14). SPIE. [ DOI:10.1117/12.543794] 19. Plaza, A., Martínez, P., Pérez, R., & Plaza, J. (2004). A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE transactions on geoscience and remote sensing, 42(3), 650-663. [ DOI:10.1109/TGRS.2003.820314] 20. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. 21. Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm-a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd. [ DOI:10.1016/B978-008045157-2/50081-X] 22. Xie, F., Li, F., Lei, C., Yang, J., & Zhang, Y. (2019). Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification. Applied Soft Computing, 75, 428-440. [ DOI:10.1016/j.asoc.2018.11.014] 23. Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471. [ DOI:10.1007/s10898-007-9149-x] 24. Chitnis, S., Mantripragada, K., & Qureshi, F. Z. (2024, July). SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing. In IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 7714-7719). IEEE. [ DOI:10.1109/IGARSS53475.2024.10640940] 25. Ashley, R. P., & Abrams, M. J. (1980). Alteration mapping using multispectral images: Cuprite mining district, Esmeralda County, Nevada (No. 80-367). US Geological Survey. [ DOI:10.3133/ofr80367] 26. Bedini, E., & Chen, J. (2023). Evaluation of EnMAP hyperspectral satellite data for epithermal alteration mapping at Cuprite-Goldfield, southwestern Nevada, USA. Journal of Hyperspectral Remote Sensing, 13(2), 262-269. http://dx.doi.org/10.29150/jhrs.v13.2.p262-269 [ DOI:10.29150/jhrs.v13.2.p262-269] 27. Schowengerdt, R. A. (2006). Remote sensing: models and methods for image processing. elsevier. 28. Chen, B., Liu, L., Zou, Z., & Shi, Z. (2023). Target detection in hyperspectral remote sensing image: Current status and challenges. Remote Sensing, 15(13), 3223. [ DOI:10.3390/rs15133223] 29. Ghamisi, P., Plaza, J., Chen, Y., Li, J., & Plaza, A. J. (2017). Advanced spectral classifiers for hyperspectral images: A review. IEEE Geoscience and Remote Sensing Magazine, 5(1), 8-32. [ DOI:10.1109/MGRS.2016.2616418] 30. Kruse, F. A. (2004, March). Comparison of ATREM, ACORN, and FLAASH atmospheric corrections using low-altitude AVIRIS data of Boulder, CO. In Summaries of 13th JPL Airborne Geoscience Workshop, Jet Propulsion Lab, Pasadena, CA (pp. 1-10). 31. Aggarwal, A., & Garg, R. D. (2015). Systematic approach towards extracting endmember spectra from hyperspectral image using PPI and SMACC and its evaluation using spectral library. Applied Geomatics, 7(1), 37-48. [ DOI:10.1007/s12518-014-0149-5] 32. Yang, L., Sun, X., Peng, L., Yao, X., & Chi, T. (2015). An agent-based artificial bee colony (ABC) algorithm for hyperspectral image endmember extraction in parallel. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), 4657-4664. [ DOI:10.1109/JSTARS.2015.2454518] |
|
| Send email to the article author |
|
|
|
| Add your comments about this article |
|
|
|
|
|
