Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA

Abstract In recent years, the classification of land change has revolutionized the ability to monitor and understand dynamic changes occurring on the Earth’s surface. Artificial intelligence (AI) techniques must improve the performance and accuracy of land change detection by extracting spectral features from several Convolutional Neural Networks (CNNs) and integrating them. In this study, AI techniques were applied to classify the land use and land cover (LULC) of the Najran city map in Saudi Arabia based on 2020 Landsat 8 satellite imagery. This was achieved using several hybrid models combining CNN and random forest (RF) models, namely AlexNet-RF and GoogLeNet-RF, as well as the combined spectral features of AlexNet-GoogLeNet with RF. The results showed that LULC classification using a hybrid system was superior to CNN and proved that the proposed hybrid system of combined spectral features extracted from AlexNet-GoogLeNet with RF provided better results than using the hybrid system proposed by AlexNet with RF and GoogLeNet with RF. The proposed hybrid system of combined spectral features extracted from AlexNet-GoogLeNet with RF achieved an accuracy of 96.95%, a Kappa coefficient of 0.9638, sensitivity of 96.95%, AUC of 98.4%, and specificity of 99.83%. The proposed hybrid methods aim to enhance the classification accuracy and increase the robustness of the system, ensuring consistent performance across diverse earth-change scenarios. It substantially impacts various domains, including environmental monitoring, disaster management, and sustainable urban planning.