## 2 Literature Review  
Extensive investigation has been carried out in the field of scene recognition, encompassing several procedures and techniques with the objective of progressing the current level of knowledge. This section examines the progression of scene recognition methodologies, the advent of deep learning architectures, and the advancements made by multimodal fusion approaches. This section also offers significant contextual information for comprehending the existing body of research, emphasizing notable discoveries, obstacles, and the progression of improvements in the subject. [PERSON] et al. [10] presented a prototype memory network to detect many scenes in one image. This was conducted by using several well-annotated images of individual scenarios. The network has three main components: a prototype learning module, an external memory that can store prototypes, and a multihead attention memory retrieval module. They obtained the best mean \(F1\) score of 57.40% using the AID dataset. [PERSON] et al. [11] suggested fine-tuning pre-trained CNNs for end-to-end aerial scene classification using transfer learning. Feature extraction from fine-tuned neural networks is followed by remote sensing picture categorization with a Support Vector Machine (SVM) model with linear and radial basis function (RBF) kernels. To reduce overfitting in pre-trained models, label smoothing regularization is used. Inception-v3, Xception, Residual Network50 (ResNet50), and DenseNet121, inception- and residual-based CNNs, are used for fine-tuning and feature extraction. The method has a classification accuracy of up to 98%, outperforming other methods. [PERSON] et al. [12] developed a depth-wise separable convolution (CSDS) network-based channel-spatial attention mechanism for aerial scene categorization. The researchers first created DS-Conv and pyramid residual connection architectures. The DS-Conv approach efficiently recovers and combines channel features, reducing processing needs. Pyramid residual connections also link features from multiple layers, enabling relationships. They use an upgraded cross-entropy loss function to reduce the influence of comparable categories during backpropagation. They obtained an accuracy of 94.29% using the AID dataset. [PERSON] et al. [13] used channel-spatial attention in a residual dense network architecture to classify remote sensing images. Initially, leftover dense blocks are used to fuse multilayer convolutional features. A channel-spatial attention module is included for better feature representation. After data augmentation, the SoftMax classifier classifies the scene. They obtained an overall accuracy of 94.15% using the AID dataset. [PERSON] et al. [14] presented a method for remote sensing image classification using vision transforms. They first split the images into smaller portions, which are flattened and embedded sequentially. The resulting sequence is then sent into numerous multiheaded attention layers to create the final representation. The initial token sequence is fed into a SoftMax classification layer. The researchers study numerous data augmentation methodsto generate more training data to improve classification performance. Researchers classified the AID dataset with 95.86% accuracy. [PERSON] and [PERSON] [15] presented the extraction of two informative feature categories from the raw RGB and saliency-coded network streams. The authors propose a deep feature fusion model that combines these two sets of features for classification. They obtained an accuracy of 93.70% using the AID dataset. [PERSON] et al. [16] presented S-MAT, a semantic-driven Masked Attention Transformer method for multilabel aerial scene image categorization. S-MAT uses a Masked Attention Transformer (MAT) to capture correlations in Semantic Disentanglement Module-generated label embeddings. In the presented MAT, the masked attention method eliminates needless dependencies and improves model resilience. Thus, the strategy presented in this work may directly and properly capture label interdependence.