# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 5 Conclusions  
Traditional remote sensing scene classification tasks have been broadened by ground environment audio and remote sensing aerial images. In this study, we presented TFAVC-Net, a two-stage fused-based multimodal audiovisual classification network for tasks involving scene classification in audiovisual remote sensing. TFAVCNet is made up of an audio module, a vision module, and a fusion module, which effectively combine the acoustic and visual modalities, and achieves state-of-the-art performance. In addition, in order to extract valuable ground environment audio information, we designed a Transformer-based Audio Transformer (AiT) network, which is different from the previous CNN or a CNN combined with a self-attention network. Additionally, in the fusion module, we designed an adaptive distributed weighted fusion module to obtain fusion embedding. Especially in the decision fusion module, we retained the single-mode decision results for both images and audio and weighted them with the feature fusion results. There is still much research to be performed in the area of remote sensing scene classification based on audiovisual fusion. Firstly, the availability of publicly accessible audiovisual multimodal datasets for remote sensing tasks is limited, and there is an urgent requirement for extensive remote sensing audiovisual multimodal datasets to support relevant research initiatives. Moreover, it is essential to explore methods for effectively modeling single-mode information when integrating visual and auditory data. There is a need to investigate how to extract more-valuable supervisory information from the coarse-level congruence of the visual and auditory modalities when they are combined. Finally, more-effective fusion methods should be designed to build the correlation between them. Because information is different, some have more information, some have less information, and the synchronicity between them is different. Conceptualization, Y.W., W.H. and M.J.; methodology, Y.W., W.H. and Y.L. ; software, Y.L. ; validation, Y.W., W.H. and M.J.; formal analysis, Y.W. and Y.L. ; investigation, Y.L. and X.Y. ; data curation, Y.L. and X.Y. ; writing--original draft preparation, Y.L. and W.H. ; writing--review and editing, Y.W., W.H. and Y.L. ; visualization, Y.W. and Y.L. ; supervision, Y.W. and W.H. All authors have read and agreed to the published version of the manuscript. This research is supported by the Natural Science Foundation of Zhejiang Province (LZ20F020003, LZ21F020003, LSZ19F010001, LY17F020003) and the National Natural Science Foundation of China (61272311, 61672466). Not applicable. The ADVANCE dataset is available at [[https://zenodo.org/records/3828124](https://zenodo.org/records/3828124)]([https://zenodo.org/records/3828124](https://zenodo.org/records/3828124)) (accessed on 24 October 2023). The SoundingEarth dataset is available at [[https://zenodo.org/records/5600379](https://zenodo.org/records/5600379)]([https://zenodo.org/records/5600379](https://zenodo.org/records/5600379)) (accessed on 24 October 2023). The accompanying code is available at [[https://github.com/IG71Y/TFAVCNet/tree/main](https://github.com/IG71Y/TFAVCNet/tree/main)]([https://github.com/IG71Y/TFAVCNet/tree/main](https://github.com/IG71Y/TFAVCNet/tree/main)) (accessed on 24 October 2023).