# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
###### Abstract
CNN architectures serve as an automated framework for feature learning and representation, facilitating the extraction of features from images in a unified spatiotemporal manner of the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) [41]. AlexNet, introduced by [PERSON] et al. in 2012 [42], emerges as the pioneering large-scale convolutional neural network (CNN) in the field of computer vision. It achieves a notable image classification performance, attaining an 83.6% top-five accuracy compared to the manual classification methods (e.g., histograms of oriented gradients (HoG) [43], scale invariant feature transform (SIFT) [44], and local binary pattern (LBP) [45]). The architecture known as GoogLeNet or Inception-v1, as detailed in the work by [46], won the ILSVRC 2014 challenge with an accuracy rate of 93.7%. Then, the classical Inception architecture evolved into both Inception-v2 and Inception-v3 [47], achieving an incredible performance with a 94.4% accuracy on the ILSVRC 2012 classification dataset, in contrast to the manual classification method SIFT [44]. A variant of Inception-v3 CNNs (denoted CMwv by [48]) has been employed for automatic SAR image classification and achieved the optimal accuracy for geophysical phenomenon detection on the public dataset. However, CMwv exhibits limited generalization capability in capturing oceanic signatures, as evidenced by its notably low precision level, approaching zero, in the detection of oceanic fronts (OF), and a recall level below 50% in the identification of pure ocean waves (PW). Moreover, the utilization of Inception-v3's image input techniques by CMwv leads to a lack of consideration for distinctive features associated with various oceanic phenomena and the domain-specific knowledge embedded in SAR images. Taken together, for a greater good on improving model reliability and guiding the spatiotemporal analysis, it is imperative to integrate oceanic scene classification with sophisticated characteristics of natural images for a combined scheme that can overcome the inefficiency of geophysical phenomenon prediction observed in several WV datasets. In this study, we present a multichannel neural network (MCNN) management scheme to mitigate the impacts of insufficient data samples on ocean SAR scene classification. Based on multichannel data augmentation, MCNN involves multiple versions of standard CNN models equipped with convolution kernels of different sizes (\(1\times 1,3\times 3,5\times 5\)), conducting convolution operations to capture image features at different scales. This design enables the model to emphasize both local and global features, thereby improving its capacity to recognize ocean SAR scenes of varying scales. Moreover, MCNN employs a comprehensive approach to extract multiple features from the original SAR images. These extracted features are then organized into multiple channels within the intricately designed Inception CNN module, effectively fulfilling the goal of capturing deeper features. Additionally, several techniques have been incorporated into MCNN, such as multi-feature fusion, data augmentation, and multichannel feature extraction, to address the challenges arising from the aforementioned inadequate data. We use a number of SAR image datasets to evaluate the performance of MCNN and validate our design strategies through experimental analysis. In summary, our paper makes the following contributions:  
1. We first illustrate and describe the wave mode data from the Sentinel-1 and TenGeoP-SARwv datasets. To cover the whole stereoscopic structure of the geophysical phenomena, we construct a new multi-dimensional dataset and data processing module that efficiently extracts, concatenates, and processes different area data from multiple satellite missions. 2. We propose a novel deep learning framework, namely, MCNN, including multi-feature fusion for capturing valuable information, data augmentation for perturbing feature embedding, and multichannel feature extraction for accurately distinguishing different types of oceanic phenomena.