## 3 Sea Surface Sun Glint Suppression and Tiny Human Object Detection Based on Visual Saliency
### Maritime Object Detection Based on Two-Step Clustering  
As shown in the representation of object saliency in Figure 5, the saliency of the maritime tiny human objects is relatively low compared with that of boats and other objects. In addition, the pixel intensity values of the set of background noise signals are much higher and close to the saliency of the targets. To perform object localization, an adaptive threshold is applied for preliminarily screening of the salient points in the whole image. Namely, localization is initially conducted based on the pixel intensity values greater than the adaptive threshold; then, the salient points of objects and partially strong noise are separated by employing two-step clustering to achieve maritime tiny object detection based on visual saliency. The salient points identified based on the adaptive threshold mainly comprise object and noise salient points, where the latter can be further divided into glint noise and background noise. Using a lower threshold can generally identify the salient points of tiny human objects, but a set of background noise signals with higher pixel intensity values can also be introduced, as shown in Figure 6b. The differences in spatial distribution characteristics between object and noise salient points can be used for maritime UAV image denoising. The spatial distribution characteristics of different salient points are as follows: Generally, background noise salient points are represented as outliers, as the amount of background noise is small and sparsely distributed in the whole image. The amount of glint noise is much larger and centrally and uniformly distributed in the area, with high pixel intensity values; however, the density of glint noise in the area is low. By comparing the uniform and sparse distribution characteristics of glint and background noise, respectively, we can conclude that their salient points are in relatively high quantity and their density characteristics are well defined. Figure 5: The pixel values of the images for HSV-gray (\(P_{\text{HSV-gray}}\)), pixel surface (\(P_{\text{gauss}}\)), and object saliency (\(P_{\text{saliency}}\)-3D). DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an unsupervised clustering method based on minimum density estimation [53]. All the points are clustered by setting two parameters of the algorithm: the distance radius (\(\varepsilon\)) and the minimum quantity of clustered points (\(P_{\min}\)). Points that do not belong to any clusters can be considered outliers. Background noise, glint noise, and objects have different spatial distribution characteristics. Therefore, two-step DBSCAN clustering is carried out to successively eliminate background and glint noise. The first step of clustering, based on large \(\varepsilon\) and small \(P_{\min}\), is to eliminate background noise that is sparsely distributed in the whole image. \(\varepsilon=150\) and \(P_{\min}=50\) were used in this study for this first step. A large \(\varepsilon\) value can identify the points distributed in local areas such as object clusters and glint clusters. The \(P_{\min}\) for this step is much smaller than those of object and glint clusters but larger than that of the background cluster. Thus, the points of background noise are too far from each other to satisfy the requirement of \(P_{\min}\) within the distance of \(\varepsilon\) for being clustered. The second step of clustering, based on small \(\varepsilon\) and large \(P_{\min}\), is to eliminate glint noise, which is centrally and uniformly distributed. \(\varepsilon=2\) and \(P_{\min}=20\) were used in this study for this second step. A small \(\varepsilon\) can identify object clusters with high density. For this step, \(\varepsilon\) is smaller than the distance among glint noise points. The points of glint noise are too far from each other to satisfy the requirement of \(P_{\min}\) within the distance of \(\varepsilon\) for being clustered.