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Ensemble of Support Vector Machines for spectral-spatial classification of hyperspectral and multispectral images

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Abstract

Previous studies on different satellite images have not yet introduced a single attribute with the highest accuracy for different applications. In this paper, a novel classification system with the highest strength against possible noises is offered using Support Vector Machine (SVM) and its performance is evaluated on the selected satellite images. So, an optimal high-strength classifier with the sufficient level of accuracy is proposed executing Composite Kernels and Ensemble of Classifiers. Results obtained from applying this method on IKONOS (91.65%) and AVIRIS (97.71%) satellite images (in Tehran and Indian Pine study areas) showed that the proposed method accuracy is higher than the Direct Summation of Kernels, Weighted Summation of Kernels, Cross Information Kernels and Extracted Features techniques. The main reason for this significant difference is the wide range and variety of input features.

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The raw/processed data required to reproduce the above findings cannot be shared at this time due to technical/ time limitations. We will share if it is requested by the journal.

Rouzbeh shad (Corresponding Author).

Seyyed Tohid Seyyed-Al-hosseini, Yaser Maghsoudi, Marjan Ghaemi.

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The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

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Authors and Affiliations

  1. Civil Engineering Department, Engineering Faculty, Ferdowsi University of Mashhad, Mashhad, Iran

    Rouzbeh Shad, Seyyed Tohid Seyyed-Al-hosseini & Marjan Ghaemi

  2. Remote sensing Department, Geodesy and Geomatics Faculty, K.N.Toosi University of Technology, Tehran, Iran

    Yaser Maghsoodi Mehrani

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  1. Rouzbeh Shad
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  2. Seyyed Tohid Seyyed-Al-hosseini
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Correspondence to Rouzbeh Shad.

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Shad, R., Seyyed-Al-hosseini, S.T., Mehrani, Y.M. et al. Ensemble of Support Vector Machines for spectral-spatial classification of hyperspectral and multispectral images. Multimed Tools Appl 82, 42119–42146 (2023). https://doi.org/10.1007/s11042-023-14972-3

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