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Article type: Research Article
Authors: Aruna Sri, P. | Santhi, V.; *
Affiliations: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Correspondence: [*] Corresponding author. V. Santhi, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India. E-mail: [email protected].
Abstract: This study addresses challenges in land use and cover identification using remote sensing (RS) imagery, focusing on the Uppal region. By leveraging deep learning models, particularly an optimized ResNext-50 architecture, we aim to enhance efficiency and accuracy in classifying land features. Our approach integrates Landsat-8 and hyper-spectral satellite data, utilizing preprocessing techniques like dark subtraction, stacking, merging, and spectral enhancement. Principal Component Analysis (PCA) is applied to streamline high-dimensional feature sets obtained from pre-processed spectral data. We further employ hybrid NSCT-FDCT fusion for integrating Landsat-8 and hyperspectral images. The resulting fused image is fed into our classification process, utilizing the modified ResNext50 (Deep Learning Architecture) model with Reptile Search Optimization for weight link optimization. Notably, our proposed method achieves impressive outcomes: 97% accuracy, 96% sensitivity, 99% specificity, 3% error, 97% precision, and a 95% Matthew Correlation Coefficient. This demonstrates the efficacy of our approach in predicting diverse land covers within the Uppal region, showcasing the potential of Landsat-8 and Hyper-spectral data for accurate land use and cover identification.
Keywords: Landsat-8, hyper-spectral, land cover, hybrid NSCT-FDCT, reptile search algorithm, resnext-50
DOI: 10.3233/JIFS-232891
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3209-3229, 2024
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