Hybrid hesitant fuzzy linguistic bi-objective binary coyote clustering based segmentation and classification for land use land cover in hyperspectral image - Yele, Vijaykumar P.; Alegavi, Sujata; Sedamkar, R. R. (International journal of information technology (Singapore. Online), 2024)
Land use and land cover segmentation and classification from hyperspectral image is significant in many land use inventories. Though several techniques have been proposed for classification of land use and land cover, the classification accuracy is low in the prevailing approaches. This manuscript offers Auto-Metric Graph Neural Network to improve the Land Use/Land Cover (AMGNN-LU/LC) classification. Land use and land cover classification is assessed with the help of EuroSAT dataset. The input image is enhanced with preprocessing method called Anisotropic Diffusion Kuwahara Filtering (ADKF). After preprocessing, Hesitant Fuzzy Linguistic Bi-objective Clustering (HFLBC) technique is utilized for segmentation. The Binary Coyote Optimization Algorithm (BCOA) is used for optimizing hesitant fuzzy linguistic bi-objective clustering. The segmented image is classified into land use/ land cover with the help of auto-metric graph neural network. The introduced method is implemented in PYTHON. The proposed approach attains 99% precision, 98.30% accuracy, 99% F1-score, 99.2% recall, 98.1% sensitivity, 99% specificity, 98% Structural Similarity Index Measure (SSIM), 44 dB Peak Signal-to-Noise Ratio (PSNR) and 2.7 s computational time and 0.12% loss. The AMGNN-LU/LC’s efficiency is compared to the prevailing approaches.