Abstract
The use of hyperspectral imaging systems in studying plant properties, types, and conditions has significantly increased due to numerous economical and financial benefits. It can also enable automatic identification of plant phenotypes. Such systems can underpin a new generation of precision agriculture techniques, for instance, the selective application of plant nutrients to crops, preventing costly losses to soils, and the associated environmental impact to their ingress into watercourses. This paper is concerned with the analysis of hyperspectral images and data for monitoring and classifying plant conditions. A spectral-texture approach based on feature selection and the Markov random field model is proposed to enhance classification and prediction performance, as compared to conventional approaches. Two independent hyperspectral datasets, captured by two proximal hyperspectral instrumentations with different acquisition dates and exposure times, were used in the evaluation. Experimental results show promising improvements in the discrimination performance of the proposed approach. The study shows that such an approach can shed a light on the attributes that can better differentiate plants, their properties, and conditions.
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References
Geladi, P.L.M., Grahn, H.F., Burger, J.E.: Multivariate images, hyperspectral imaging: background and equipment. In: Techniques and Applications of Hyperspectral Image Analysis, pp. 1–15. John Wiley and Sons, Ltd. (2007)
ElMasry, G., Sun, D.W.: Principles of Hyperspectral Imaging Technology. In: Sun, D.W. (ed.) Hyperspectral Imaging for Food Quality Analysis and Control, pp. 3–43. Academic Press, San Diego (2010)
Campbell, J., Wynne, R.: Introduction to Remote Sensing, 5th edn. Guilford Publications, New York (2011)
Liu, H., Lee, S.H., Chahl, J.S.: Development of a proximal machine vision system for off-season weed mapping in broadacre no-tillage fallows. J. Comput. Sci. 9(12), 1803–1821 (2013)
Duchesne, C., Liu, J., MacGregor, J.: Multivariate image analysis in the process industries: a review. Chemometr. Intell. Lab. Syst. 117, 116–128 (2012)
Lu, G., Fei, B.: Medical hyperspectral imaging: a review. J. Biomed. Opt. 19(1), 010901 (2014)
Geladi, P., Bengtsson, E., Esbensen, K., Grahn, H.: Image analysis in chemistry i. Properties of images, greylevel operations, the multivariate image. TrAC Trends Anal. Chem. 11(1), 41–53 (1992)
Qin, J.: Hyperspectral Imaging Instruments. In: Sun, D.W. (ed.) Hyperspectral Imaging for Food Quality Analysis and Control, pp. 129–172. Academic Press, San Diego (2010)
Bharati, M.H., Liu, J., MacGregor, J.F.: Image texture analysis: methods and comparisons. Chemometr. Intell. Lab. Syst. 72(1), 57–71 (2004)
AlSuwaidi, A., Veys, C., Hussey, M., Grieve, B., Yin, H.: Hyperspectral feature selection ensemble for plant classification. In: Hyperspectral Imaging and Applications (HSI 2016), October 2016
Yin, H., Allinson, N.M.: Self-organised parameter estimation and segmentation of MRF model-based texture images. In: Proceedings of the IEEE International Conference on Image Processing, ICIP 1994, vol. 2, pp. 645–649. IEEE (1994)
AlSuwaidi, A., Veys, C., Hussey, M., Grieve, B., Yin, H.: Hyperspectral selection based algorithm for plant classification. In: 2016 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 395–400, October 2016
Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Norwell (1998)
Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17(4), 491–502 (2005)
Hall, M.A., Smith, L.A.: Feature selection for machine learning: Comparing a correlation-based filter approach to the wrapper. In: Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference, pp. 235–239 (1999)
Blake, A., Kohli, P., Rother, C.: Markov Random Fields for Vision and Image Processing. The MIT Press, Cambridge (2011)
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)
Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–6(6), 721–741 (1984)
Foster, D.H., Amano, K., Nascimento, S.M.C.: Color constancy in natural scenes explained by global image statistics. Vis. Neurosci. 23(3–4), 341–349 (2006)
Mahlein, A.K., Hammersley, S., Oerke, E.C., Dehne, H.W., Goldbach, H., Grieve, B.: Supplemental blue led lighting array to improve the signal quality in hyperspectral imaging of plants. Sensors 15(6), 12834–12840 (2015)
Kulkarni, S., Harman, G.: An Elementary Introduction to Statistical Learning Theory, 1st edn. Wiley Publishing, New Jersey (2011)
Gitelson, A., Merzlyak, M.N.: Spectral reflectance changes associated with autumn senescence of aesculus hippocastanum l. and acer platanoides l. leaves. spectral features and relation to chlorophyll estimation. J. Plant Physiol. 143(3), 286–292 (1994)
Mahlein, A.K., Rumpf, T., Welke, P., Dehne, H.W., Plmer, L., Steiner, U., Oerke, E.C.: Development of spectral indices for detecting and identifying plant diseases. Remote Sens. Environ. 128, 21–30 (2013)
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AlSuwaidi, A., Grieve, B., Yin, H. (2017). Towards Spectral-Texture Approach to Hyperspectral Image Analysis for Plant Classification. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_28
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DOI: https://doi.org/10.1007/978-3-319-68935-7_28
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