SEMSF-Net: Explainable Squeeze–Excitation Multiscale Fusion Network for Aerial Scene and Coastal Area Recognition Using Remote Sensing Images

Land Use and Land Cover (LULC) classification plays a key role in the last decade for managing the decay of resources and mitigating the impact of population growth. It is used in several places, such as rapid urbanization, agriculture, climate change, coastal areas, and disaster recovery. The traditional remote sensing (RS) techniques encounter limitations in accurately classifying dynamic and complex Ariel Scenes such as coastal areas and LULC. This paper proposed a novel Squeeze-Excitation Multi-Scale Fusion Network (SEMSF-Net) to classify LULC and the coastal regions using remote sensing images. The proposed model is based on the squeeze-excitation block initially embedded with inception and dense blocks separately. These blocks are designed based on the multi-scale to generate more important features information that can later perform accurate classification. In the next phase, these blocks are fused at the network level, where bottleneck and inverted residual blocks are connected to reduce the learnable parameters and improve feature strength. The hyperparameters of this network are selected based on the several experiments utilized in the training of the proposed model. The trained SEMSF-Net architecture is employed further in the testing phase, and classification is performed. The GradCAM is also used to interpret the trained model's visual prediction. Three datasets are utilized for the experimental process: the Coastal dataset, MLRSNet, and NWPU. We obtained an improved accuracy of 94.94, 93.7, and 95.70% on these datasets, respectively. In addition, the macro recall rate is 79.0, 93.0, and 96%, respectively. Comparison with several recent techniques shows the proposed model outperforms the selected datasets.