A novel deep Siamese framework for burned area mapping Leveraging mixture of experts

Abstract

Due to the complexity of the areas and the diversity of the objects, traditional Burned Area Mapping (BAM) methods cannot provide promising results. Moreover, these methods focus on additional processing to improve their results, which is time-consuming and complex. Therefore, an advanced framework is needed to achieve accurate results in burned area mapping. In this context, this study proposes a novel Siamese-based mixture of expert networks for burned area mapping, which takes advantage of a mixture of experts (MoE) and position and channel attention mechanisms for deep feature generation. The proposed framework has two deep feature extractor channels to account for the bi-temporal multispectral pre- and post-fire input datasets. To evaluate the performance of the SMoE model, three multispectral Sentinel-2 were used in different countries. The results were compared with other advanced machine and deep learning models, including Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), 2D-Siamse Convolutional Neural Network (2D-SCNN), and 3D-Siamse Convolutional Neural Network (3D-SCNN). The result of the burned mapping shows that the proposed model has a high effectiveness compared to other models, as it provides an average accuracy of more than 98% and 0.91 b y overall accuracy (OA) and kappa coefficient (KC) indices, respectively.