Abstract
Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds.
Highlights
With the development of remote-sensing technology, the quality of remote-sensing images acquired has become very high, while the high spatial-resolution remote-sensing images are increasingly used in various fields [1,2]
In view of the above problems, this study proposes a ship detection and recognition method based on the modified hypercomplex Flourier transform (MHFT) saliency model
We proposed a novel ship target automatic detection and recognition based on hypercomplex Flourier transform (HFT)
Summary
With the development of remote-sensing technology, the quality of remote-sensing images acquired has become very high, while the high spatial-resolution remote-sensing images are increasingly used in various fields [1,2]. It is very useful to achieve important strategic target recognition on remote-sensing images (e.g., ships, airports, aircraft, etc.) [3,4]. The general target recognition algorithm is not suitable for use in remote-sensing images, in order to recognize meaningful targets in spatial resolution remote-sensing images, this study proposes a novel target recognition algorithm. Traditional remote sensing image ship detection methods are based on gray threshold segmentation and grayscale statistics [6,7,8]. These methods are suitable for uniform sea surface textures, low water surface gray value and high contrast between the sea surface and ships
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
