Proper land-cover and deforestation classification is crucial in environmental protection, sustainable land use, and climate change. Nevertheless, the current deep learning and remote sensing models are usually limited by poor spectral-spatial feature integration, high computational complexity, and poor feature selection, which reduces the classification accuracy. To address these limitations, this paper presents a new model that combines SpectroRes-Former, a SpectralSpatial Residual Transformer Network, and QuFiSel (QuantumFisher Selection), a hybrid feature selection method. The SpectroRes-Former effectively learns both spatial and spectral dependencies with a ResNet encoder, spectral-spatial attention fusion, and Transformer-based contextual learning. At the same time, QuFiSel improves the discrimination of features by integrating Quantum-Inspired Feature Selection (QIFS) and Fisher Score, so that the most informative features are selected to classify. The effectiveness of the approach is tested based on the Indian Pines hyperspectral image, which consists of 220 bands after typical band selection and 16 land cover types. The performance is measured through 5-fold cross-validation and the statistical validation through mean, standard deviation, and paired tests. The obtained average classification accuracy of the proposed method equals 99.21 ± 0.08%, along with the Precision value of 99.08%, Recall 99.16%, F1-score 99.12%, and Matthews Correlation Coefficient 0.991, demonstrating superior results compared to some of the most competitive state-of-the-art techniques. The paired t-test gives p < 0.05, which confirms the statistical significance of the result. It is shown that the introduced SpectroRes-Former framework can be used effectively for hyperspectral land cover classification while providing high generalization. The offered solution can serve as a reliable base for smart environmental monitoring systems.