## 2 Related Work  
Previous classification models, such as neural networks and support vector machines (SVMs) [7], typically featured a network structure with either one hidden layer node or none at all, earning them the designation of \"shallow\" classification models. However, these shallow classification models are categorized under shallow learning and possess limited capability to extract deeper features from constrained datasets, thereby constraining their overall model generalization ability [8]. In contrast, deep learning, as a novel machine learning paradigm, aspires to emulate the analytical learning capabilities of the human brain. By leveraging substantial volumes of training data and employing deep models with multiple hidden layers, deep learning can uncover more valuable features, consequently enhancing classification accuracy. Unlike shallow learning, deep learning architectures are characterized by their depth, typically consisting of more than three layers of hidden nodes. This depth enables them to explore deeper and more abstract features, thereby acquiring more precise feature information and ultimately affording superior generalization capabilities. In recent years, deep learning has achieved remarkable success in various image classification applications, prompting an increasing number of researchers to apply it to the domain of remote sensing image processing. Deep learning has been integrated into the classification of hyperspectral data to harness the wealth of spectral information within hyperspectral images [1]. Notably, [PERSON] et al. [9] introduced an innovative classification model that leverages both spectral and  
Figure 1: Oceanic internal waves in MODIS. spatial information present in hyperspectral data. This approach effectively mitigates the issue of rapid overfitting and accuracy degradation typically encountered when using convolutional neural networks (CNNs) with limited training data. [PERSON] et al. [10] employed the faster R-CNN framework, incorporating convolutional neural network features, to detect oceanic internal waves. Their efforts resulted in an impressive recognition rate of 94.78%. On a related note, [PERSON] et al. [11] harnessed the lightweight convolutional neural network MobileNetv2 to extract deep and abstract image features. By combining feature fusion with bilinear pooling, they achieved higher accuracy in the realm of remote sensing image classification while utilizing fewer parameters and computational resources, surpassing other state-of-the-art methods. In scenarios involving small-sample datasets, [PERSON] et al. [12] introduced a novel fault-tolerant deep learning approach known as RSSC-ETDL for remote sensing image scene classification. This method effectively mitigates the adverse effects stemming from inaccurately labeled datasets. In recent years, researchers have also proposed many excellent models. In 2022, [PERSON] et al. [13] proposed a stripe segmentation algorithm based on SegNet for synthetic aperture radar (SAR) images. This method effectively identifies the presence of oceanic internal waves in SAR images and accurately locates both light and dark stripes associated with these waves. Also using SAR images, [PERSON] et al. [14] construct a comprehensive dataset of 390 Sentinel-1 synthetic aperture radar (SAR) images, spanning multiple oceanic regions. These images are used to develop a machine learning model achieving high precision and recall when applied to detect internal waves (IW) across different scales and propagation directions in SAR imagery. Also in that year, [PERSON] et al. [15] conducted an analysis using a collection of optical multispectral satellite images, including Sentinel-2 and Landsat-8 data, in conjunction with sea-truth data to identify internal wave features within the Black Sea. Deep learning models typically necessitate multiple iterations of data analysis and processing, often involving substantial amounts of data [16].