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Land use/land cover change classification and prediction using deep learning approaches

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Abstract

Nowadays, land use and land cover (LULC) change is a major problem for decision-makers and ecologists on account of its impact on natural ecosystems. In this manuscript, LU/LC change classification and prediction using deep convolutional spiking neural network (DCSNN) and enhanced Elman spike neural network (EESNN) (LU/LC-DCSNN-EESNN) is proposed. The input images are gathered from IRS Satellite Resourcesat-1 LISS-III with Cartosat-1 digital elevation model (DEM) satellite imagery of the Javadi Hills, Tamil Nadu. After that, the images are pre-processed using the fast discrete curvelet transform and wrapping (FDCT-WRP) method is used for extracting the region of interest (ROI) coordinates of Javadi Hills satellite image. Then, for categorizing the area of forest and non-forest, the DCSNN is used. The categorized images are given to post-classification process for eradicating the noise and misclassification errors by Markov chain random field (MCRF) co-simulation approach. The LU/LC changes are predicted using EESNN method. The performance metrics, like precision, accuracy, f1 score, error rate, specificity, recall, kappa coefficient and ROC, are analyzed. The proposed LU/LC-DCSNN-EESNN method has attained 19.45%, 20.56% and 23.67% higher accuracy, 19.45%, 32.56% and 17.45% higher F-measure, and 16.78%, 22.09% and 32.39% lower error rate compared with the existing methods.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Vadapalani, Chennai, Tamil Nadu, India

    P. Adlene Ebenezer & S. Manohar

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  1. P. Adlene Ebenezer
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  2. S. Manohar
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AEP contributed to conceptualization, methodology and writing—original draft preparation. MS performed supervision.

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Correspondence to P. Adlene Ebenezer.

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Ebenezer, P.A., Manohar, S. Land use/land cover change classification and prediction using deep learning approaches. SIViP 18, 223–232 (2024). https://doi.org/10.1007/s11760-023-02701-0

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