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Automated Lightweight Descriptor Generation for Hyperspectral Image Analysis

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Abstract

Analyzing hyperspectral images poses a non-trivial challenge due to various challenges. To overcome most of these challenges one of the widely employed approach involves utilizing indices, such as the Normalized Difference Vegetation Index (NDVI). Indices provide a powerful means to distill complex spectral information into meaningful metrics, facilitating the interpretation of specific features within the hyperspectral domain. Moreover, the indices are usually easy to compute. However, creating indices for discerning arbitrary data classes within an image proves to be a challenging task. In this paper, we present an algorithm designed to automatically generate lightweight descriptors, suited for discerning between arbitrary classes in hyperspectral images. These lightweight descriptors within the algorithm are characterized by indices derived from selected informative layers. Our proposed algorithm streamlines the descriptor generation process through a multi-step approach. Firstly, it employs Principal Component Analysis (PCA) to transform the hyperspectral image into a three-channel representation. This transformed image serves as input for a Segment Anything Model (SAM). The neural network outputs a labeled map, delineating different classes within the hyperspectral image. Subsequently, our Informative Index Formation algorithm (INDI) utilizes this labeled map to systematically generate a set of lightweight descriptors. Each descriptor within the set is adept at distinguishing a specific class from the remaining classes in the hyperspectral image. The paper demonstrates the practical application of the developed algorithm for hyperspectral image segmentation.

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REFERENCES

  1. Manolakis, D.G., Lockwood, R.B., and Cooley, T.W., Hyperspectral Imaging Remote Sensing: Physics, Ssensors, and Algorithms, Cambridge: Cambridge Univ. Press, 2016.

    Book  Google Scholar 

  2. Shippert, P., Introduction to hyperspectral image analysis, Online J. Space Commun., 2003, vol. 2, no. 3, p. 8.

    Google Scholar 

  3. Mary B. Stuart, A.J.S. McGonigle, and J.R. Willmott, Hyperspectral imaging in environmental monitoring: A review of recent developments and technological advances in compact field deployable systems, Sensors, 2019, vol. 19, no. 14, p. 3071. https://doi.org/10.3390/s19143071

  4. Lu, B., Dao, P., Liu, J., He, Y., and Shang, J., Recent advances of hyperspectral imaging technology and applications in agriculture, Remote Sens., 2020, vol. 12, no. 16, p. 2659. https://doi.org/10.3390/rs12162659

    Article  Google Scholar 

  5. Peyghambari, S. and Zhang, Y., Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: An updated review, J. Appl. Remote Sens., 2021, vol. 15, no. 03. https://doi.org/10.1117/1.jrs.15.031501

  6. Bioucas-Dias, J.M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., and Chanussot, J., Hyperspectral remote sensing data analysis and future challenges, IEEE Geosci. Remote Sens. Mag., 2013, vol. 1, no. 2, pp. 6–36. https://doi.org/10.1109/mgrs.2013.2244672

    Article  Google Scholar 

  7. Wu, C., Niu, Z., Tang, Q., and Huang, W., Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation, Agr. Forest Meteorol., 2008, vol. 148, nos. 8–9, pp. 1230–1241. https://doi.org/10.1016/j.agrformet.2008.03.005

    Article  Google Scholar 

  8. Li, K.Y., Toward automated machine learning-based hyperspectral image analysis in crop yield and biomass estimation, Remote Sens., 2022, vol. 14, no. 5, p. 1114. https://doi.org/10.3390/rs14051114

    Article  Google Scholar 

  9. Liu, H., Zhu, Hongchun, Li, Z., and Yang, G., Quantitative analysis and hyperspectral remote sensing of the nitrogen nutrition index in winter wheat, Int. J. Remote Sens., 2020, vol. 41, no. 3, pp. 858–881. https://doi.org/10.1080/01431161.2019.1650984

    Article  Google Scholar 

  10. Yanmin Y., Na, W., Youqi, C., Yingbin, H., and Pengqin, Tang., Soil moisture monitoring using hyper-spectral remote sensing technology, 2010 Second IITA International Conference on Geoscience and Remote Sensing, 2010, vol. 2, pp. 373–376. https://doi.org/10.1109/iita-grs.2010.5604219

  11. Liu, N., Guo, Y., Jiang, H., and Yi, W., Gastric cancer diagnosis using hyperspectral imaging with principal component analysis and spectral angle mapper, J. Biomed. Opt., 2020, vol. 25, no. 06, p. 1. https://doi.org/10.1117/1.jbo.25.6.066005

    Article  Google Scholar 

  12. Halicek, M., Fabelo, H., Ortega S., Callico G.M., and Fei B., In-vivo and ex-vivo tissue analysis through hyperspectral imaging techniques: Revealing the invisible features of cancer, Cancers, 2019 vol. 11, no. 6, p. 756. https://doi.org/10.3390/cancers11060756

    Article  Google Scholar 

  13. Aboughaleb, I.H., Aref, M.H., and El-Sharkawy, Y.H., Hyperspectral imaging for diagnosis and detection of ex-vivo breast cancer, Photodiagn. Photodyn. Ther., 2020, vol. 31, p. 101922. https://doi.org/10.1016/j.pdpdt.2020.101922

    Article  Google Scholar 

  14. Qureshi, R., Uzair, M., Khurshid, K., and Yan, H., Hyperspectral document image processing: Applications, challenges and future prospects, Pattern Recognit., 2019, vol. 90, pp. 12–22. https://doi.org/10.1016/j.patcog.2019.01.026

    Article  Google Scholar 

  15. Fei-Fei, L., Deng, J., and Li, K., ImageNet: Constructing a large-scale image database, J. Vision, 2010, vol. 9, no. 8, pp. 1037–1037. https://doi.org/10.1167/9.8.1037

    Article  Google Scholar 

  16. Graña, M., Veganzons, M.A., and Ayerdi, B., Hyperspectral Remote Sensing Scenes – Grupo De Inteligencia Computacional (GIC), www.ehu.eus. http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes.

  17. Asrar, G., Fuchs, M., Kanemasu, E.T., and Hatfield J.L., Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat 1, Agron. J., 1984, vol. 76, no. 2, pp. 300–306. https://doi.org/10.2134/agronj1984.00021962007600020029x

    Article  Google Scholar 

  18. Rondeaux, G., Steven, M., and Baret, F., Optimization of soil-adjusted vegetation indices, Remote Sens. Environ., 1996, vol. 55, no. 2, pp. 95–107. https://doi.org/10.1016/0034-4257(95)00186-7

    Article  Google Scholar 

  19. Paringer, R., Mukhin, A.V., and Kupriyanov, A., Formation of an informative index for recognizing specified objects in hyperspectral data, Comput. Opt., 2021, vol. 45, no. 6, pp. 873–878. https://doi.org/10.18287/2412-6179-co-930

    Article  Google Scholar 

  20. Rodarmel, C. and Shan, J., Principal component analysis for hyperspectral image classification, Surv. Land Inf. Sci., 2002, vol. 62, no. 2, pp. 115–122.

    Google Scholar 

  21. Kirillov, A., Segment Anything, arXiv (Cornell University), 2023. https://doi.org/10.48550/arxiv.2304.02643

  22. Mukhin, A., Paringer R., and Ilyasova, N., Feature selection algorithm with feature space separability estimation using discriminant analysis, 2021 International Conference on Information Technology and Nanotechnology (ITNT), 2021, pp. 1–4. https://doi.org/10.1109/itnt52450.2021.9649144

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Funding

This work was supported by the Ministry of Science and Higher Education of the Russian Federation, project no. FSSS-2024-0016.

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  1. Samara National Research University, 443086, Samara, Russia

    Artem Mukhin, Rustam Paringer, Danil Gribanov & Igor Kilbas

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  1. Artem Mukhin
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  2. Rustam Paringer
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  3. Danil Gribanov
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  4. Igor Kilbas
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Correspondence to Artem Mukhin.

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Artem Mukhin, Paringer, R., Gribanov, D. et al. Automated Lightweight Descriptor Generation for Hyperspectral Image Analysis. Opt. Mem. Neural Networks 33, 264–275 (2024). https://doi.org/10.3103/S1060992X24700164

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  • DOI: https://doi.org/10.3103/S1060992X24700164

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