# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 1 Introduction  
Remote sensing offers a variety of data sources for surface feature recognition and classification, enhancing our understanding of the Earth's surface [1; 2]. Due to the advancement of remote sensing satellites and various social media, traditional remote sensing scene classification tasks are typically based on images, using passive or active sensors [3; 4; 5; 6] and various street view images. In recent years, urban planning [7], environmental monitoring [8; 9], and various other fields have greatly benefited from the extensive acquisition of remote sensing data and the rapid advancements in remote sensing technology. On the other hand, as a result of the ongoing development of sound recording software and the rising demand for various acoustic tasks, scene classification tasks based on audio have advanced significantly [10; 11; 12], such as speech command recognition [13], emotion recognition [14], and audio event classification [15; 16]. When classifying an acoustic scene, the input is often a brief audio recording; however, when classifying a visual scene, the input can be either an image or a brief video clip. Advanced audio scene-classification algorithms currently in use are typically based on spectral features, most frequently log-Mel spectrograms, and convolutional neural network structures, as CNN structures aid in providing general classification information for longer segments of acoustic scenes [17]. The conventional CNN architecture may execute downsampling operations on the time dimension and feature dimension while continually learning acoustic information using several convolutional layers and pooling layers. Based on this, [PERSON] et al. [18] proposed a dilated convolutional kernel that allows achieving a comparable receptive field without intermediate pooling to improve the performance of the acoustic scene classification system in the context of local feature acquisition. In the following study, [PERSON] et al. [19] confirmed that restricting the size of the receptive fieldcan effectively improve the performance of acoustic scene classification. Researchers have extended the commonly used CNN structure in anticipation of improving the learning ability of acoustic features. [PERSON] and [PERSON] [20] drew inspiration from the field of computer vision, adopted a spatial pyramid pooling strategy, pooled and combined features at different spatial resolutions, and further improved the performance of acoustic feature extraction. The attention mechanism was also used to build an audio scene classification system in addition to the CNN structure and its variations for acoustic scene classification. An attention mechanism allows the neural network to concentrate on a particular subset of input data because different time segments of ambient audio recordings have varying impacts on the categorization of acoustic scenes. [PERSON] et al. [21] introduced a multi-level attention model in the network's feature-learning section to improve feature-learning performance. At the same time, the attention mechanism can also be applied to the pooling operation of the feature map. [PERSON] et al. [22] used Self-Determination CNNs (SD-CNNs) to identify the acoustic events due to overlapping higher uncertainty frame information. In contrast, remote sensing image classification methods have a longer history and a greater variety of methodologies, which are either based on the traditional handcrafted features or the deep learning ones. Early scene classification mainly depended on manually created features. There are numerous traditional techniques for extracting handcrafted features, including the Histogram of Oriented Gradients (HOG) [23], texture descriptors [24], GIST [25], color histograms [26], and Scale-Invariant Feature Transform (SIFT) [27]. Deep learning methods have also been introduced into the field of remote sensing. [PERSON] et al. [28] first used Convolutional Neural Networks (CNN) for scene classification of high-resolution remote sensing images. [PERSON] et al.