Enhancing land cover classification via deep ensemble network

Abstract

The rapid adoption of drones has transformed industries such as agriculture, environmental monitoring, surveillance, and disaster management by enabling more efficient data collection and analysis. However, existing UAV-based image scene classification techniques face limitations, particularly in handling dynamic scenes, varying environmental conditions, and accurately identifying small or partially obscured objects. These challenges necessitate more advanced and robust methods for land cover classification. In response, this study explores ensemble learning (EL) as a powerful alternative to traditional machine learning approaches. By integrating predictions from multiple models, EL enhances accuracy, precision, and robustness in UAV-based land use and land cover classification. This research introduces a two-phase approach combining data preprocessing with feature extraction using three advanced ensemble models DenseNet201, EfficientNetV2S, and Xception employing transfer learning. These models were selected based on their higher performance during preliminary evaluations. Furthermore, a soft attention mechanism is incorporated into the ensembled network to optimize feature selection, resulting in improved classification outcomes. The proposed model achieved an accuracy of 97 %, precision of 96 %, recall of 96 %, and an F1-score of 97 % on UAV image datasets. Comparative analysis reveals a 4.2 % accuracy improvement with the ensembled models and a 1 % boost with the advanced hybrid models. This work significantly advances UAV image scene classification, offering a practical solution to enhance decision-making precision in various applications. The ensemble system demonstrates its effectiveness in remote sensing applications, especially in land cover analysis across diverse geographical and environmental settings.