# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 1 Introduction  
Over the ocean, oceanic phenomena affect sea surface roughness [1; 2; 3; 4; 5; 6]. Synthetic aperture radar (SAR) can estimate the sea surface roughness by backscattering, so various oceanic phenomena can be observed from SAR images, including both natural oceanic phenomena such as oceanic eddies, oceanic fronts, rain cells, and oil spills, and artificial oceanic phenomena, such as ship wakes. The detection of various phenomena using SAR images is one of the key research areas in oceanic applications. The features of oceanic phenomena in SAR images are affected by environmental conditions and satellite parameters. For example, oceanic eddies appear as bright and dark features in SAR images due to wind direction [7]. Wind speed affects the strength of the features of oceanic phenomena in SAR images [8]. Satellite parameters such as different bands, polarizations, and incidence angles also have an effect on the features of oceanic phenomena in SAR images [9; 10; 11]. Due to the influence of these factors, the features exhibited by oceanic phenomena are very complicated, which makes the detection of oceanic phenomena difficult. Traditional methods [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] for detecting oceanic phenomena in SAR images are based on handcrafted features and detection thresholds. The features and thresholds used usually need to bedesigned for a class of oceanic phenomena in a sea area, so the generalization ability is poor. In addition, traditional methods are susceptible to noise interference. It is difficult to efficiently extract features and set appropriate thresholds when there is noise in the image. In response to the above problems, researchers have applied artificial intelligence methods [23; 24; 25; 26] to the detection of oceanic phenomena in SAR images. In recent years, deep learning methods in artificial intelligence have gradually become mainstream. Image features do not need to be manually modeled, but rather, deep features of the image are extracted through multilevel self-learning, which can effectively address the high degree of feature similarity and large geometric differences in oceanic phenomena. The introduction of deep learning methods to the detection of oceanic phenomena in remote sensing images can greatly improve accuracy. [PERSON] et al. [27] applied convolutional neural networks to realize the effective detection of oceanic fronts in sea surface temperature (SST) images. [PERSON] et al. [28] proposed a network called EddyNet, which has an accuracy of 89.83% for oceanic eddies detection in sea surface height (SSH) images. [PERSON] et al. [29] developed a detection framework for oceanic eddies in sea level anomaly (SLA) images. [PERSON] et al. [30; 31] proposed an oceanic eddies detection network for SAR images. Its optimal detection accuracy is 95%, which is much higher than the 80% accuracy obtained by traditional methods on the same dataset. [PERSON] et al. [32; 33] used a network called inception-v3 for category information detection of oceanic phenomena in SAR images, achieving greater than 90% accuracy, and discussed how to apply these detection results. Although the deep learning methods have shown excellent performance in the detection of oceanic phenomena in SAR images, there are still some shortcomings. Regarding current research, there is a lack of SAR image datasets of oceanic phenomena that have been annotated by professional experts. In addition, few studies have used deep learning methods to detect oceanic phenomena in SAR images. Previous studies have only detected one type of oceanic phenomenon, or only extracted category information, which does not meet the growing demand. As a step forward, we use images acquired by the Sentinel-1 satellite to create a sample dataset of oceanic phenomena in SAR images and propose a network called a multifeature fusion neural network (MFNN) to detect oceanic phenomena by fusing multilevel features and multiscale features.