# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
###### Abstract
To tackle the aforementioned issues, deep neural network techniques have been well-studied to alleviate the influence of speckle noise and backscattering features [27]. For example, a deep encoder-decoder convolutional neural network (CNN) architecture  
Figure 1: Comparison of an original SAR image with its denoised version. Image (**a**), depicting an aerial perspective of the terrain, represents the original version sourced from the TerraSAR-X dataset. This image is characterized by severe speckle noise, impairing the clarity of image features. In contrast, image (**b**) showcases the denoised version, revealing more intricate details and highlighting features such as edges and texture. The comparison indicates that the denoised image exhibits enhanced features, making it better suited for utilization in image recognition tasks [20]. is established for the speckle filtering tasks [28], which outperformed classical algorithms on both simulated and real data. A noisy reference-based SAR deep learning filter was proposed [29], achieving a better despeckling performance over certain state-of-the-art deep learning techniques. But when these deep learning methods are simply applied to SAR image-based detection, some important SAR domain knowledge and corresponding oceanic features are eliminated by specific feature extraction and transformation [30]. Compared to deep learning strategies, manual classification and recognition significantly depend on visual features [31], introducing inefficiencies in processing marine big data. Each visual task necessitates predefined artificial features, resulting in time-consuming efforts. Furthermore, these manually defined features are relatively low-level when compared to those extracted by deep learning methods, thereby constraining the comprehensive representation of marine objects. Therefore, for effective utilization of SAR images by scientific users, it is critical to develop an appropriate deep learning framework for standardizing the use of SAR images so that they can be seamlessly integrated into the repertoire of the public oceanic datasets for category information analysis. Recently, data augmentation has been extensively studied to synthetically enhance the diversity of training datasets by a magnitude of 1000 within image rotation, random transformation, and horizontal flip [32]. Furthermore, several efforts have explored the use of data augmentation to further boost the performance of artificial intelligence methods for automatic SAR image classification and oceanic phenomenon detection [33; 34]. In addition, the transfer-learning strategy has been verified as an efficient way to develop a robust CNN model for specific applications in the case of limited datasets [23]. Hence, an effective oceanic phenomenon recognition system also needs to provide an augmentation mechanism for SAR scene detection applications to efficiently expand the data size without costly data collection. In addition, the routine SAR wave mode (WM) measurements from multiple satellite missions typically exhibit a series of fine spatial resolution (4 m), large scene footprints (20 km \(\times\) 20 km), and high signal-to-noise ratios, which improves SAR imagery despeckling effects [35]. Because the massive wave mode data at the global scale, approximately 120 k images per month, contain redundant geophysical information, it is feasible to dynamically capture the useful geophysical properties by a dynamic method that combines feature extraction utilizing the wavelet-radon transform (WR) and classification employing the neural network technique, specifically the dynamic neural network (DNN) [36]. The conventional image processing methods are commonly based on visual inspection and handcrafted features [37; 38], which are notably impractical for the generalization of the structural patterns of different geophysical phenomena [39; 40].