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Classification of Crops Based on Band Quality and Redundancy from the Hyperspectral Image

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Agriculture-Centric Computation (ICA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1866))

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Abstract

Crop classification from hyperspectral remote sensing images is an effective means to understand the agricultural scenario of the country. Band selection (BS) is a necessary step to reduce the dimensions of the hyperspectral image. We propose a band selection method that takes into account the image quality in terms of a non-reference quality index along with correlation analysis. The optimum bands selected using the proposed method are then fed to the three supervised machine learning classifiers, namely, support vector machine, K-nearest neighbours and random forest. We have also investigated the impact of correlation analysis by showing the comparison of the proposed band selection method with another variant of our method where correlation analysis is not included. The result shows that the crop classification shows better performance in terms of overall accuracy and kappa coefficient when image quality and correlation analysis are both considered while selecting optimum bands. All the experiments have been performed on the three hyperspectral datasets, Indian Pines, Salinas and AVIRIS-NG, which contain major crop classes. The results show that the optimum bands selected using the proposed method provide the highest overall accuracy, equal to 89.63% (Indian Pines), 95.88% (Salinas) and 97.44% (AVIRIS-NG). The overall accuracy shows a rise from +2% to +4% to that of bands without considering correlation analysis. The advantage of this band selection method is that it does not require any prior knowledge about the crop to select the bands.

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Authors and Affiliations

  1. Institute of Technology, Nirma University, Ahmedabad, India

    Kinjal Dave & Yogesh Trivedi

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  1. Kinjal Dave
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  2. Yogesh Trivedi
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Correspondence to Kinjal Dave .

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Editors and Affiliations

  1. Indian Institute of Technology Ropar, Ropar, India

    Mukesh Kumar Saini

  2. Indian Institute of Technology Ropar, Ropar, India

    Neeraj Goel

  3. Indian Institute of Technology Guwahati, Guwahati, India

    Hanumant Singh Shekhawat

  4. Polytechnic University of Valencia, Valencia, Spain

    Jaime Lloret Mauri

  5. Saint Louis University, St. Louis, MO, USA

    Dhananjay Singh

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Dave, K., Trivedi, Y. (2023). Classification of Crops Based on Band Quality and Redundancy from the Hyperspectral Image. In: Saini, M.K., Goel, N., Shekhawat, H.S., Mauri, J.L., Singh, D. (eds) Agriculture-Centric Computation. ICA 2023. Communications in Computer and Information Science, vol 1866. Springer, Cham. https://doi.org/10.1007/978-3-031-43605-5_12

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  • DOI: https://doi.org/10.1007/978-3-031-43605-5_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43604-8

  • Online ISBN: 978-3-031-43605-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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