# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 1 Introduction
[29] introduced deep learning to process high-resolution remote sensing images, proposed a method for automatically learning feature representation, and experimentally demonstrated its effectiveness in scene classification tasks. Inspired by the mechanism of attention, [PERSON] et al. [5] introduced a recurrent attention mechanism combined with deep learning methods to solve the scene classification problem of high-resolution remote sensing images, providing a new method for image processing and classification in the field of remote sensing. Vision Transformer (ViT) has recently been used for scene classification and has shown good results. For image classification tasks, the Vision Transformer (ViT) model uses attention-based deep learning. The ViT model uses a self-attention mechanism to capture the global information in the image, in contrast to the conventional Convolutional Neural Network (CNN), which views the image as a collection of segmented blocks. At the moment, researchers are attempting to use Transformers for remote sensing tasks. For example, [PERSON] et al. [30] proposed a Transformer method based on channel-spatial attention for remote sensing image scene classification; [PERSON] et al. [31] proposed an efficient multi-scale Transformer named EMT-CAL and a cross-level attentional learning method for remote sensing scene classification; [PERSON] et al. [32] proposed a method called Reformer for remote sensing scene classification. This method aims to improve the accuracy and effectiveness of remote sensing image scene classification by combining a residual network and Transformer. However, whether applied to audio scene classification or remote sensing image classification, prevailing methodologies predominantly rely on data from a singular modality. In practical, real-world scenarios, human perception and comprehension of natural environments inherently involve the integration of multiple sensory modalities, particularly sight and hearing. Human perceptual research underscores the inherent complementarity of sound and visual information in the comprehensive understanding of environmental scenes [33]. For example, within an airport setting, aerial images captured from the air by aircraft provide an expansive, bird's-eye view of the airport's layout. At the same time, people can hear some ground environment sounds including human conversations, airport announcements, and unique engine sounds on the ground. In light of the foregoing analysis grounded in common sense, it is reasonable to explore the amalgamation of sound and visual information for the purpose of scene classification. Researchers are increasingly interested in multimodal data fusion due to the continuous evolution of re mote sensing and sensor instrumentation. Emerging as a valuable sensory data source, ground environment audio data can contribute significantly to the classification of surface scenes by furnishing information on environmental acoustics, such as wildlife vocalizations and traffic sounds. This auditory information can help classify various scenes. Hence, researchers have initiated the exploration of sound-image fusion for multimodal remote sensing scene classification, striving to enhance classification precision and robustness [34]. [PERSON] et al. [35] proposed a method of integrating local context information into a self-attention model to improve scene classification, using local and global temporal relationships between video frames to obtain a more-comprehensive contextual representation of a single frame. In addition to relying on the attention mechanism to improve the accuracy of multi-modal scene classification, technical improvement on the feature extraction level is another research hotspot. [PERSON] et al.