## 3 RS Applications in Ocean
### Ship Detection (SD)
#### 3.6.2 SAR  
SAR systems have also been extensively utilized for SD due to their operational ability in all weather conditions and all times [314]. However, compared to optical imagery, they contain a higher amount of noise. Consequently, it is difficult to detect small ships using SAR data. Moreover, there are relatively few SAR satellites compared to optical systems, causing challenges for real-time ship tracking and monitoring [314]. Numerous algorithms have been developed for SD using SAR images, including coherent target decomposition [331], polarimetric entropy [332], wavelet transforms [333], and generalized-likelihood ratio test [334]. However, the most-used method in this regard is the Constant FAR (CFAR) [335]. The CFAR algorithm searches for bright pixels in SAR images by comparing their grayscale value to the surrounding pixels. The most important step in the CFAR method is to design a distribution model for the background to specify an associated Probability Density Function (PDF). The Gaussian distribution, K-distribution, and Gamma PDFs have been widely used in this regard. Then, the Probability of False Alarm (PFA) for the threshold T is calculated by Equation (4). \[PFA=1-\int\limits_{-\infty}^{T}f_{pdf}(x)dx=\int\limits_{T}^{\infty}f_{pdf}(x)dx \tag{4}\]  
The conventional CFAR algorithm only considers the intensity value of the pixels. However, this can be due to the SAR ambiguities and ocean clutter in complex ocean conditions [320; 321]. Therefore, FAR increases by incorrectly detecting bright clutter pixels as ships and missing some not-that-bright ship pixels due to factors such as speckles, incident angels, and materials of targets. With the advent of high spatial resolution SAR systems, such as TerraSAR-X and Sentinel-1, researchers are encouraged to take advantage of spatial relationships alongside the intensity of pixels to improve the conventional CFARalgorithm. For example, [PERSON] et al. [336] proposed the bilateral CFAR algorithm, which used kernel density estimation as an analytical tool to determine the structure of the targets. Then, a combined value was generated using both intensity and spatial distributions. Finally, the standard CFAR method was applied to the SAR images with combined values to detect targets. Their results indicated that the bilateral CFAR reduced FAR compared to the conventional CFAR. Moreover, [PERSON] et al. [337] proposed an Intensity-Space (IS) domain CFAR SD algorithm based on two factors [337]: (1) two neighboring pixels are more likely to be of the same kind, and (2) two pixels with similar intensity have a higher probability of belonging to the same class. In the IS-CFAR algorithm, intensity and locality were considered at the same time, as opposed to the sequential approach of the bilateral CFAR algorithm, and thus, the image was mapped into the IS domain to create a new transformed image. Then, the CFAR method was applied to extract ship targets. Finally, the results were refined using three target features of the target region area, form factor, and aspect ratio to further eliminate false alarms. The results showed that this method performed better than the bilateral CFAR only in terms of true target detection precision. DL methods have also been widely investigated for SD using SAR data. For example, [PERSON] et al. [338] proposed an end-to-end, densely connected multiscale neural network based on Faster-RCNN for SD in SAR images [338]. They densely connected feature maps to each other from top to down, instead of using one feature map for region proposal, to achieve multiscale and multiscene (inshore and offshore) SD without ocean-land segmentation. They also proposed a training scheme to decrease the weight of easy-to-detect targets to decrease FAR in SD. Their results on multiresolution public SAR data, including imagery from RADARSAT-2, TerraSAR-X, Sentinel-1, and GaoFen-3, indicated the effectiveness of the method and its superiority over the Faster-RCNN algorithm.