LB-DL method: A hybrid framework of Laplacian-Beltrami with deep learning models for spectral–spatial enhancement of wildfire monitoring remote sensing imagery

Satellite imagery and remote sensing are suitable tools for monitoring and surveillance of the Earth. The use of this data to address various environmental challenges, from surveillance to risk management, is of great interest. In one of the applications of interest, satellite imagery is used for monitoring and assessing wildfires. However, its effectiveness is often limited by the spatial-spectral quality of the images, which also affects the performance of deep learning. This study proposes the LB-DL method, a hybrid framework that applies the Laplace-Beltrami (LB) operator to this application and integrates it with deep learning models to evaluate the improvement of image quality using LB. The LB operator exploits the inherent geometric structure of image manifolds for this improvement by suppressing noise while simultaneously enhancing critical spatial-spectral features, leading to more accurate mapping of burned areas. The method was evaluated using Sentinel-2 multispectral images of a wildfire in Uzbekistan and compared with conventional and state-of-the-art deep learning models. To test the robustness, realistic noise scenarios, additive, multiplicative, and dead pixels were simulated. Performance was measured using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and classification accuracy. The results showed that LB-DL outperformed the baseline methods under the evaluated conditions, achieving PSNR improvements of up to 4.4 dB and SSIM increases of 0.13. Further validation on the hyperspectral datasets of Indian Pines and Salinas suggested promising generalizability, though additional validation on more diverse datasets and real-world conditions is needed. These findings highlight the potential of integrating geometry-based denoising with deep learning to enhance the reliability of remote sensing applications such as wildfire monitoring.