# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 1 Introduction
### Preprocessing Methods for Ship Detection  
As noted earlier, remote sensing images have diverse backgrounds and complex spatial structures. Thus, different methods of image preprocessing are often required before various tasks can be performed [4; 5], including image sharpening processing, contrast enhancement and cloud removal processing, etc. In [6], an N-D probability density function matching technique for the preprocessing of multitemporal images is introduced in the remote sensing domain. It can retain the data correlation structure after the probability density function matching. In addition, [PERSON]. [7] designed a preprocessing technique to enhance the local information of the original image data. A new image structure represented by a fuzzy function is utilized to encode the information into an image. [PERSON]. [8] proposed a preprocessing algorithm that both smooths noise and enhances edges. It consists of an improved adaptive spatially-weighted filter that can achieve both of the above functions. In [9], image preprocessing steps such as filtering, upsampling, and band registration were standardized by providing references. These steps suggest that preprocessing is an important part of many applications based on remote sensing images.  
Although there are many preprocessing methods for different tasks, scene classification is still the most basic method. It can mine more targeted and valuable information, and save us from using one method to solve all problems [10]. Hence, target ship detection could benefit from this significant progress. Traditionally, scene classification methods based on object recognition are approaches that require prior information about the objects. In [11], a multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery is devised in order to classify scenes. In this method, the complementary features can be effectively combined and appropriate low-level feature descriptions can be provided for the semantic representations. It is interesting and superior to single-features methods.  
Furthermore, deep learning-based methods have became the choice for solving many computer vision and remote sensing problems [12]. The authors of [13] have devised a key region or location capturing method. This method can be combined with different CNN models and prevent confusion of different categories. On the other hand, in [14], a density peak clustering algorithm (DPC) is proposed for preprocessing hyperspectral image classification. By using a density peak clustering algorithm, the HSI pixels are automatically subdivided into smaller classes, and the within-class difference caused by spectral variation is reduced. In addition, we observed that image blocks in different scenes have different grayscale histograms, as illustrated in Figure 1. This intuitive mapping relationship provides us with an idea. We take the gray histograms of all image patches as the input and then use the DPC algorithm to classify different scenes. This can quickly and effectively classify different scenes.