# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
###### Abstract  
High-resolution synthetic aperture radars (SARs) are becoming an indispensable environmental monitoring system to capture the important geophysical phenomena on the earth and sea surface. However, there is a lack of comprehensive models that can orchestrate such large-scale datasets from numerous satellite missions such as GaoFen-3 and Sentinel-1. In addition, these SAR images of different ocean scenes need to convey a variety of high-level classification features in oceanic and atmospheric phenomena. In this study, we propose a multichannel neural network (MCNN) that supports oceanic SAR scene classification for limited oceanic data samples according to multi-feature fusion, data augmentation, and multichannel feature extraction. To exploit the multichannel semantics of SAR scenes, the multi-feature fusion module effectively combines and reshapes the spatiotemporal SAR images to preserve their structural properties. This fine-grained feature augmentation policy is extended to improve the data quality so that the classification model is less vulnerable to both small- and large-scale data. The multichannel feature extraction also aggregates different oceanic features convolutionally extracted from ocean SAR scenes to improve the classification accuracy of oceanic phenomena with different scales. Through extensive experimental analysis, our MCNN framework has demonstrated a commendable classification performance, achieving an average precision rate of 96%, an average recall rate of 95%, and an average F-score of 95% across ten distinct oceanic phenomena. Notably, it surpasses two state-of-the-art classification techniques, namely, AlexNet and CMwv, by margins of 23.7% and 18.3%, respectively. s +
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SAR images inherently exhibit speckle noise due to the attenuation of echo signals, which is characterized by a distribution of granular patterns [15]. This noise is typically characterized as multiplicative, not only affecting SAR images but also influencing all coherent images. Therefore, the primary challenge with the automated classification of SAR images becomes speckle noise interference. On the one hand, the noise poses difficulties in multiple tasks such as feature extraction and edge detection [16]. This interference not only diminishes the contrast of the images but also modifies the spatial statistics of the underlying scene backscatter, thereby compromising the interpretation and recognition of targets. On the other hand, the speckle noise interference poses a big challenge for oceanic scene classification with multiple SAR images and deteriorates the reliability of prediction models like predicting meteorological conditions and analyzing atmospheric patterns [17; 18; 19]. As depicted in Figure 1, the filtered image (Figure 1b) reveals enhanced details, in contrast to the original SAR image (Figure 1a), illustrating improvements achieved through noise removal. Due to the small differences in backscattering characteristics [21], it is difficult to precisely distinguish the specific ocean phenomenon from similar marine environments. Moreover, the surface current features cannot be extracted because of the wind to backscatter data in dual-polarized mode [22]. Furthermore, there is presently a lack of comprehensive annotated datasets for SAR targets. This limitation arises from the expensive nature of acquiring SAR data and the associated high costs involved in ensuring quality annotation [23]. Hence, relatively insufficient labeled data are used for training and modeling due to the long acquisition period and expensive labeling process [24]. Two recent works report that most previous ocean SAR image-based applications just involve limited regional scene case studies such as those conducted in the East China Sea and the Malacca Strait, where the SAR images are employed for the identification of ocean eddies and the analysis of the spatial-temporal distribution to investigate the characteristics of internal solitary waves [25; 26].