# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 2 On-Board Ship Detection Based on Deep Learning  
In the fields of image classification and object detection, the Convolution Neural Network (CNN) has made a great breakthrough in performance and efficiency. AlexNet [14] won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [27] 2012 by a large margin. After several years of development, the error rate of the winning scheme [28] in ImageNet classification competition reached 3.57% in 2015, while the error rate of human is 5.1%. In recent years, deep learning technology has been successfully applied in various fields. [PERSON] et al. first proposed an object detection framework based on Region-based Convolutional Networks (R-CNN) [29] in 2014, and then researchers proposed a series of object detection algorithms based on deep learning, such as Fast R-CNN [30], Faster R-CNN [16], You Only Look Once (YOLO) [31], Single Shot MultiBox Detector (SSD) [32], etc. These methods have surpassed the traditional algorithms in various object detection tasks.  
Owing to the good performance of deep learning methods in object detection from natural scene images, an increasing number of methods based on deep learning are applied to remote sensing image processing. The deep learning algorithm has achieved good performance in the detection tasks of aircraft [33], oil tanks [34] and vehicles [35] from remote sensing images. Deep learning based ship detection methods have also been proposed [18; 19; 20; 21; 22; 23; 24; 25; 26]. These methods have achieved outstanding results in performance, but most of them deal with images on the ground without considering the computational and storage resources.  
Compared with objects in natural scene image and other targets in remote sensing image, on-board ship detection from remote sensing images has the following difficulties:  
* Due to the limitation of the Graphic Processing Unit (GPU) memory, the large-scale remote sensing image should be cropped into image patches. For images of different sizes, object detection methods from natural scene image are usually performed by rescaling the long side of input image to a certain length, such as 800 pixels. As the size of remote sensing image ranges from several thousands to tens of thousands of pixels, it is not feasible to use the remote sensing images with high down-sampling rate.
* The ship target varies from a few meters to a few hundred meters in length and has different directions. Since the ship target usually has a rigid body with symmetrical strip structure, it has a large aspect ratio.
* The ship target has large intraclass variations. Due to different requirements, the structure of ship body is designed into different shapes, which presents great differences in remote sensing images.
* The final detection bounding boxes need to be accurately located, and include the complete body of ship for further analysis in ground equipment. If the downlinked target patches miss ships or only include parts of ships, some valuable information will be lost.  
To solve these difficulties, we adopt the strategies shown in Figure 2. We first crop the large scale remote sensing images into a series of 1024 \(\times\) 1024 image patches, and then a lightweight model is employed to do feature extraction. The region proposals are generated by the Region Proposal Network (RPN) with square anchors. After Region of Interest (ROI) pooling layer and global average pooling layer, the bounding box regression and score of proposals are processed, followed by Bigger-Left Non-Maximum Suppression (BL-NMS) to remove redundant detection. Finally, the results in image patches are mapped into the input image coordinate to obtain the final result.  
This paper focuses on ship detection on sea surface from optical remote sensing image. Compared with traditional synthetic aperture radar (SAR) images, optical images have the advantages of rich details and short revisit period. In addition, since the ships far away from the land has more important value than the inshore ship, our method mainly aims at offshore ships.