Aerial Scene Recognition From Remote Sensing Images Based on Hybrid Multitemporal Information Fused Transformer-FRACTAL Network

Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. For accurate classification of aerial scenes from remote sensing images, image classification must effectively integrate local spatial details, context-dependent information from global regions, and hierarchical representations of different scenes. This study proposes a hybrid multitemporal information fusion architecture for aerial scene recognition from Remote Sensing images. The proposed model combines the modified version of EfficientNet-B0 (truncated to a maximum number of convolutional layers), Vision Transformer (ViT), and FractalNet to leverage the strengths of these three models. Each model's branch runs in parallel and is fused via depth-wise feature concatenation. Extensive experiments are conducted on four benchmark datasets (EuroSAT, NWPU, EarthHazards, and Turkey Earthquake), demonstrating the integrated model's improved performance over traditional CNN and transformer models, as well as its compact nature (approximately 2.8 million parameters) compared to these models. Extensive ablation studies also validate the effectiveness of the combined approaches of block-depth selection, self-attention layer, and fusion strategy. The results from the combined model show that it is an effective and efficient approach to classify large numbers of Remote Sensing viewpoint scene types.