## 3 RS Applications in Ocean
### Ship Detection (SD)
#### 3.6.1 Optical  
With the advent of high-resolution optical systems, the volume of high spatiotemporal resolution imagery is growing, making them suitable for SD and monitoring applications [314]. Although optical RS systems can only operate during the daytime, recently, there has been a considerable focus on SD using optical imagery due to increasing the number of Very High Resolution (VHR) optical sensors [327]. There are a variety of methods for SD using optical images, such as threshold-based methods [328], anomaly detection [321], transform domain methods [313], Bayesian decision [329], shape and texture [314], and visual saliency [330]. In this study, the methods based on visual saliency and shape and texture are discussed in detail due to their relatively good performance and popularity.  
A common approach for SD using optical images is applying the methods based on shape and texture features. These methods are robust and provide high accuracy in SD. However, false alarms (wakes and ocean clutter) still exist. So far, several studies have used the shape and texture features extracted from VHR optical satellite images for SD. For example, [PERSON] et al. [314] proposed an SD method based on shape and texture features using a combination of China-Brazil Earth Resources Satellite (CBERS) and SPOT images with a hierarchical coarse-to-fine FAR elimination process. They first carried out global and  
Figure 9: A general ship detection method using spaceborne RS data.  
local image segmentation. Then, a simple shape analysis (e.g., region area and aspect ratio) was employed to remove obvious false candidates. Subsequently, finer shape features (e.g., compactness, rectangularity, and moment invariants), texture features (e.g., simple texture, wavelet-based, Multiscale Gaussian Differential Features (MGDFs)), and a novel operator, called local multiple pattern, proposed based on local binary pattern, were employed. Finally, they adopted a semisupervised hierarchical classification using SVM to identify ships.  
Another popular approach in SD using optical satellite data is implementing methods based on visual saliency. Inspired by the human visual system, the visual saliency method tries to extract candidate regions that attract human eyes [330]. For example, [PERSON] et al. [330] presented a method for extracting salient signals using a Phase spectrum of Fourier Transform (PFT). The binary saliency map was calculated through an adaptive segmentation. Then, the image was homogenized using a homogeneous filter to make it smooth. Finally, the Otsu segmentation method was applied to extract ship candidate regions. For discriminating ship targets, they developed a novel descriptor, called Ship Histogram of Oriented Gradient (S-HOG), based on the symmetrical shape of the ship. Their results on GaoFen-1 satellite images showed that the method was robust in detecting ships of various sizes in different ocean conditions.  
As discussed, other than the abovementioned traditional methods, DL algorithms along with optical data have been recently used for SD. For instance, [PERSON] et al. [325] proposed a method based on Faster-RCNN to detect small ships and gather ships in offshore and inland water scenes to cover shortcomings of the traditional approaches of SD. They first classified the images into water and nonwater areas and created small ROIs that might contain ships using the SVM classifier. Then, the ROIs were ingested into an RCNN-based algorithm. They also modified the network architecture of the Faster-RCNN by combining multiresolution convolutional features and performing ROI pooling on a larger feature map. Their modified Faster-RCNN showed higher precision than various state-of-the-art SD methods, including the original Faster-RCNN.