# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 2 Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images  
### Overview  
This paper studies the detection of five types of oceanic phenomena: Oceanic eddies, rain cells, oceanic fronts, ship wakes, and oil spills. Oceanic eddies and rain cells are approximated as evenly distributed surface targets, oceanic fronts and ship wakes are linear targets, and oil spills have both forms. Due to their different physical and geometric properties, various oceanic phenomena exhibit different characteristics in SAR images, and these characteristics have an important impact on oceanic phenomena detection. First, the differences in the backscattering characteristics of oceanic phenomena are small, contributing to poor distinguishability among different oceanic phenomena.  
Second, the formation of oceanic phenomena is affected by a variety of oceanic elements, which causes similar oceanic phenomena in different sea areas to exhibit different characteristics. Third, there are also huge differences in the shape and scale of similar oceanic phenomena. Finally, there are also situations in which various oceanic phenomena are superimposed.  
To account for the weak distinction and changeable characteristics of the oceanic phenomena in SAR images, we extract multilevel features and employ multilevel features for detection. It is difficult to distinguish oceanic phenomena with similar features using only low-level features for detection. However, if only deep-level features are used for detection, oceanic phenomena with smaller scales become easily lost in the background noise during the downsampling process of feature extraction. Therefore, it is necessary to perform multilevel image feature extraction to obtain deep-level features and low-level features.  
For the scale difference and superposition of the oceanic phenomena in SAR images, we extract multiscale features and obtain local and global information at different scales. Using only singlescale feature information is not sufficient to detect multiple phenomena with different scales and cannot cope with the problem of superposed oceanic phenomena. Moreover, oceanic phenomena are distributed targets with insignificant features in SAR images, and detection results can become easily disconnected. By extracting multiscale features, it is possible to detect the phenomena from the overall information and avoid detecting phenomena as multiple phenomena due to local information interference.  
For the above reasons, we propose the MFNN to detect oceanic phenomena with reference to the DeepLab [34; 35; 36; 37] series and U-net [38], which are effective networks in the field of image detection. The MFNN includes multilevel features extraction, multiscale features extraction, fusion and decision, and parameter optimization modules. The network structure of the MFNN is shown in Figure 1.  
The multilevel features extraction module uses ResNet-50 [39] to extract image features. ResNet-conv1-5 in Figure 1 represents the five blocks of ResNet-50. Considering the small number of samples in the dataset, selecting a medium-size network such as ResNet-50 can meet the requirements of multilevel features extraction, effectively limiting the complexity of the model and avoiding over-fitting. Then, atrous spatial pyramid pooling [40] (ASPP) extracts multiscale features. Atrous convolution [41] can reduce information loss when extracting multiscale features while ensuring that the resulting multichannel feature maps have the same resolution. Finally, multilevel features and multiscale features are fused by multiple convolutions, and the fusion results are evaluated by the softmax function to obtain the detection results. The weight parameters of the MFNN need to be trained and tuned before detection applications. Therefore, we use the weighted balance cross-entropy loss function and stochastic gradient descent (SGD) to optimize the MFNN parameters. The following sections detail the various parts of MFNN.  
Figure 1: The structure of the multifeature fusion neural network (MFNN).