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An Optimized Combination of Spectral and Spatial Features for Hyperspectral Images Classification via Arithmetic Optimization Algorithm

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Metaheuristics and Optimization in Computer and Electrical Engineering

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1077))

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Abstract

In hyperspectral images, spectral data can be combined with spatial details such as shape and texture for improved classification. Using a probabilistic platform, this paper proposes an approach for merging spatial (texture and shape) and spectral characteristics. Features of shape are extracted by MPs and fFeatures of texture are extracted by Gabor filters. The support vector machine (SVM) can classify these features separately, allowing an estimation of the probabilities for each pixel. To weigh integrations of these probabilities, the Developed Arithmetic Optimization Algorithm (AOA) has been developed. There are three parameters a, b, c that define how the features are effected in proposed integration. AOA calculates these parameters' optimal values. Pavia University, Indian Pines, and Salinas datasets are utilized to assess the proposed scheme. Classifying hyperspectral images using the proposed integration has been demonstrated to be effective, particularly with few labeled samples. Furthermore, comparing this method to some previously studied semi supervised classification approaches, it is more accurate.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran

    Reza Seifi Majdar

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  1. Reza Seifi Majdar
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Correspondence to Reza Seifi Majdar .

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Editors and Affiliations

  1. Department of Industrial Engineering, Ankara Yıldırım Beyazıt University (AYBU), Ankara, Türkiye

    Navid Razmjooy

  2. Department of Industrial Engineering, Ankara Yıldırım Beyazıt University (AYBU), Ankara, Türkiye

    Noradin Ghadimi

  3. Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India

    Venkatesan Rajinikanth

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Majdar, R.S. (2023). An Optimized Combination of Spectral and Spatial Features for Hyperspectral Images Classification via Arithmetic Optimization Algorithm. In: Razmjooy, N., Ghadimi, N., Rajinikanth, V. (eds) Metaheuristics and Optimization in Computer and Electrical Engineering. Lecture Notes in Electrical Engineering, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-42685-8_12

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