This study proposes and evaluates a hybrid remote-sensing architecture that combines classical multithreaded image preprocessing with a Qiskit-based quantum-circuit simulation workflow for multispectral land-cover classification. The research addresses the growing interest in integrating quantum-inspired computing techniques into geospatial data analysis while providing a controlled comparison with a conventional classification approach under identical input conditions. A Landsat 9 scene (LC91540312023193LGN00) covering the Tashkent region was processed using three widely adopted spectral indices—Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI)—to identify four land-cover categories: bare soil, vegetation, water, and built-up areas. Both the classical baseline and the simulator-based variational classifier employed the same image subset, feature representation, and preprocessing pipeline to ensure a fair evaluation. Classification performance was assessed using Overall Accuracy (OA), Cohen's Kappa coefficient, and mean Intersection over Union (mIoU), while computational efficiency was evaluated by measuring the execution time of the classification stage. The simulator-based implementation achieved more balanced class separation and higher aggregate accuracy, Kappa, and mIoU values than the selected classical baseline. The recorded classification-stage times were 4.535 s and 0.300 s, respectively, corresponding to a classical-to-simulator runtime ratio of 15.12. Because the quantum circuit was executed on a classical simulator rather than a physical quantum processing unit (QPU), this difference is reported as an implementation-specific software observation rather than evidence of quantum computational advantage. The proposed workflow provides a transparent and reproducible framework for evaluating hybrid classical and quantum-inspired approaches for multispectral remote-sensing applications and establishes a methodological basis for future studies using real quantum hardware.