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A faster SVM classification technique for remote sensing images using reduced training samples

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Abstract

The support vector machine (SVM) has been used as an efficient tool in data mining tasks during the last 2 decades. It is also used for supervised classification with reasonable accuracy. The training time and testing time using an SVM are highly computationally expensive for the classification of large data sets, like multi-band remote sensing images, whether forecasting data, health care data, social welfare data, etc. Due to this problem, researchers have used different training sample reduction methods for the faster classification of more massive data sets with reasonable accuracy. This paper presents a new faster SVM classification method for the remote sensing multi-spectral satellite image that is applied to extract suitable support vectors from the extensive training input by reducing the training time rather than traditional SVM. This method overcomes the classification problem of sizeable remote sensing data sets with better results. We have designed an efficient training sample reduction algorithm to reduce the SVM classifier training time used in the binary classification of large data sets having the number of features two and three, respectively. This binary classification approach has been extended to multi-class classification by identifying valid pairs of classes using a hierarchical clustering algorithm. Experimental findings on different 2D discrete datasets, and remote sensing class datasets performed better than Traditional SVM in terms of training time, classification time, and other performance parameters. The proposed technique is significantly better than traditional SVM for these different datasets. In terms of training time, classification time, and other performance parameters, we have also contrasted our proposed approach with the other three comparable methods.

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Data is available from the authors on reasonable request.

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Author notes

  1. Debasis Chaudhuri and Arup Kumar Pal contributed equally to this work.

Authors and Affiliations

  1. Computer Science and Engineering Department (CSED), Thapar Institute of Engineering and Technology (TIET), Bhadson Road, Patiala, Punjab, 147004, India

    Kuntal Chowdhury

  2. Department of Computer Science and Engineering, Techno India University, EM-4, Sector-V, Salt Lake, Kolkata, West Bengal, 700091, India

    Debasis Chaudhuri

  3. Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines) [IIT(ISM)], Dhanbad, Jharkhand, 826004, India

    Arup Kumar Pal

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  1. Kuntal Chowdhury
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Correspondence to Kuntal Chowdhury.

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Chowdhury, K., Chaudhuri, D. & Pal, A.K. A faster SVM classification technique for remote sensing images using reduced training samples. J Ambient Intell Human Comput 14, 16807–16827 (2023). https://doi.org/10.1007/s12652-023-04689-4

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