Developing satellite hyperspectral image processing using a maximum abundance classifier with nine ground truth classes




hyperspectral image processing, maximum abundance classifier, ground truth, satellite maps


In the hundreds of bands of the photographed substance, hyperspectral imaging delivers a great density of spectral data. This allows the images to be used for a variety of purposes, including agriculture, geosciences, and biomedical imaging. Previous work didn’t discuss the best classifier with sufficient ground truth classes. This work presents the application of maximum abundance classification (MAC) for classifying a variety of areas over hyperspectral images. The allocation of an end-member throughout hyperspectral images can be described with abundance maps. Since each pixel's abundance values represent the proportion of each end-member that is present in that pixel, the pixels in a hyperspectral image will be classified in this study by determining the highest abundance rate of every pixel and allocating it to the corresponding end-member category. The ground truth classes are represented by nine end-members in the test data: Bitumen, Shadows, Self-Blocking Bricks, Bare Soil, highlighted Metal area, Gravel, Meadows, Trees, and Asphalt. By uniformly distributing the range of wavelength over the amount of spectral domains, we initially determine the central wavelength for each band to visualize loaded data and the end-member signatures of nine ground truth classes. Next, we estimate the end-members abundance maps. Finally, we classify the Max Abundance of every pixel to present a color-coded image, the overlaid, and the classified hyperspectral image areas over their category labels. The result demonstrates that brick, bare soil, trees, and asphalt zones have all been correctly identified in the photographs, which is beneficial for the identification or detection of materials

Supporting Agency

  • The authors would like to express their deepest gratitude to the Department of Clinical Laboratory Sciences, College of Pharmacy, University of Mosul-Iraq for their support to complete this research.

Author Biographies

Ghassan Ahmad Ismaeel, University of Mosul

Master of Computer Engineering

Department of Clinical Laboratory Sciences

College of Pharmacy

Mina Basheer Gheni, Al-Nahrain University

Master of Computer Science

Department of Computer

Saad Qasim Abbas, Al-Turath University College

Master in Medical Instrument Engineering

Department of Medical Instrument Engineering Technique

Mustafa Musa Jaber, Dijlah University College

PhD, Lecturer

Department of Medical Instruments Engineering Techniques

Mohammed Hasan Ali, Imam Ja’afar Al-Sadiq University

PhD, Lecturer

Department of Computer Systems and Software Engineering


  1. Sarić, R., Nguyen, V. D., Burge, T., Berkowitz, O., Trtílek, M., Whelan, J. et al. (2022). Applications of hyperspectral imaging in plant phenotyping. Trends in Plant Science, 27 (3), 301–315. doi:
  2. Sugawara, S., Sugizaki, S., Nakayama, Y., Taniguchi, H., Ishimaru, I. (2021). Preliminary study for detection of adhesive on a painted ceramic plate and varnish on printed paper using near-infrared hyperspectral imaging at wavelengths of 1.0–2.35 µm. Infrared Physics & Technology, 117, 103809. doi:
  3. Xue, Q., Qi, M., Li, Z., Yang, B., Li, W., Wang, F., Li, Q. (2021). Fluorescence hyperspectral imaging system for analysis and visualization of oil sample composition and thickness. Applied Optics, 60 (27), 8349. doi:
  4. Shijer, S. S., Sabry, A. H. (2021). Analysis of performance parameters for wireless network using switching multiple access control method. Eastern-European Journal of Enterprise Technologies, 4 (9 (112)), 6–14. doi:
  5. Mohammed, A. B., Al-Mafrji, A. A. M., Yassen, M. S., Sabry, A. H. (2022). Developing plastic recycling classifier by deep learning and directed acyclic graph residual network. Eastern-European Journal of Enterprise Technologies, 2 (10 (116)), 42–49. doi:
  6. Guda, M., Gasser, S., El-Mahallawy, M. S., Shehata, K. (2020). FPGA Implementation of L1/2 Sparsity Constrained Nonnegative Matrix Factorization Algorithm for Remotely Sensed Hyperspectral Image Analysis. IEEE Access, 8, 12069–12083. doi:
  7. Gao, L., Zhang, B., Sun, X., Li, S., Du, Q., Wu, C. (2013). Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data. EURASIP Journal on Advances in Signal Processing, 2013 (1). doi:
  8. Lv, N., Han, Z., Chen, C., Feng, Y., Su, T., Goudos, S., Wan, S. (2021). Encoding Spectral-Spatial Features for Hyperspectral Image Classification in the Satellite Internet of Things System. Remote Sensing, 13 (18), 3561. doi:
  9. Iyer, P., A, S., Lal, S. (2021). Deep learning ensemble method for classification of satellite hyperspectral images. Remote Sensing Applications: Society and Environment, 23, 100580. doi:
  10. Hagag, A., Fan, X., Abd El-Samie, F. E. (2017). HyperCast: Hyperspectral satellite image broadcasting with band ordering optimization. Journal of Visual Communication and Image Representation, 42, 14–27. doi:
  11. Arellano, P., Tansey, K., Balzter, H., Boyd, D. S. (2015). Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. Environmental Pollution, 205, 225–239. doi:
  12. Shin, J., Kim, K., Ryu, J.-H. (2020). Comparative study on hyperspectral and satellite image for the estimation of chlorophyll a concentration on coastal areas. Korean Journal of Remote Sensing, 36 (2_2), 309–323. doi:
  13. Yang, M.-D., Huang, K.-H., Tsai, H.-P. (2020). Integrating MNF and HHT Transformations into Artificial Neural Networks for Hyperspectral Image Classification. Remote Sensing, 12 (14), 2327. doi:
  14. Huang, H., Chen, M., Duan, Y. (2019). Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding. Remote Sensing, 11 (9), 1039. doi:
  15. Veligandan, S. K., Rengasari, N. (2018). Hyperspectral image segmentation based on enhanced estimation of centroid with fast K-means. The International Arab Journal of Information Technology, 15 (5), 904–911. Available at:,%20No.%205/10400.pdf
  16. Lin, L., Chen, C., Xu, T. (2020). Spatial-spectral hyperspectral image classification based on information measurement and CNN. EURASIP Journal on Wireless Communications and Networking, 2020 (1). doi:
Developing satellite hyperspectral image processing using a maximum abundance classifier with nine ground truth classes




How to Cite

Ismaeel, G. A., Gheni, M. B., Abbas, S. Q., Jaber, M. M., & Ali, M. H. (2023). Developing satellite hyperspectral image processing using a maximum abundance classifier with nine ground truth classes. Eastern-European Journal of Enterprise Technologies, 1(2 (121), 14–20.