LightFire-Net: Efficient Cross-Modal Spectral–Spatial Information Fusion for Real-Time Wildfire Detection in Remote Sensing

Wildfires are among the most devastating natural disasters, causing significant damage to the environment, the economy, wildlife, and human lives, and requiring an early detection system. This paper presents a novel LightFire-Net deep cross model fusion architecture for Wildfire classification and interpretation from remote sensing images. The proposed model is computationally efficient and employs Efficient Flame Pattern blocks with depthwise separable convolutional layers, a Lightweight spectral spatial module, and Efficient Feature Fusion for real-time wildfire classification and detection. The model's multi-stage architecture enables hierarchical feature extraction, resulting in more accurate and efficient classification. Bayesian Optimization (BO) is employed to select hyperparameters, thereby improving the training of the proposed model on selected remote sensing datasets. A Softmax classification layer is employed for the final prediction. The proposed model achieves exceptional performance with above 98% prediction accuracy while maintaining only 1.37 million parameters and an inference speed of 7.74 ms. A comprehensive evaluation across multiple dataset splits (90:05:05-40:30:30) demonstrates robust generalization, with accuracy ranging from 98.02% to 99.25%. The five-fold mean accuracy further confirms the model's stability, at 98.62%±0.15%. The proposed model outperforms existing CNN and transformer-based architectures while being significantly more efficient, making it highly suitable and reliable for real-time monitoring in resource-constrained environments.