Abstract
Maritime target detection from optical remote sensing images plays an important role in related military and civil applications and its weakness lies in its compromised performance under complex uncertain conditions. In this paper, a novel hierarchical ship detection method is proposed to overcome this issue. In the ship detection stage, based on Entropy information, we construct a combined saliency model with self-adaptive weights to prescreen ship candidates from across the entire maritime domain. To characterize ship targets and further reduce the false alarms, we introduce a novel and practical descriptor based on gradient features, and this descriptor is robust against clutter introduced by heavy clouds, islands, ship wakes as well as variation in target size. Furthermore, the proposed method is effective for not only color images but also gray images. The experimental results obtained using real optical remote sensing images have demonstrated that the locations and the number of ships can be determined accurately and that the false alarm rate is greatly decreased. A comprehensive comparison is performed between the proposed method and the state-of-the-art methods, which shows that the proposed method achieves higher accuracy and outperforms all the competing methods. Furthermore, the proposed method is robust under various backgrounds of maritime images and has great potential for providing more accurate target detection in engineering applications.
Highlights
Maritime ship target detection and recognition by Unmanned Airborne Vehicles (UAVs) and satellites is an active research field and plays a crucial role in a spectrum of related military and civil applications, such as naval defense and security, traffic surveillance, maritime rescue, protection against illegal fisheries, anti-smuggling efforts, oil discharge control, and sea pollution monitoring, for which automatic ship detection and ship recognition are important to the protection of coastlines and exploration of the vast and rich marine resources.Ship targets are mainly divided into three categories based on the types of images: synthetic aperture radar (SAR) images, infrared (IR) images and visible images [1]
We subjectively compare the results of our combined saliency map (CSM) model with those of other models according to visual impression
To generate clearer contour and more uniform salient salient target regions, a combination of the saliency models is constructed which fuses the merits of target regions, a combination of the saliency models is constructed which fuses the merits of the two the two models through a self-adaptive threshold based on Entropy information
Summary
Maritime ship target detection and recognition by Unmanned Airborne Vehicles (UAVs) and satellites is an active research field and plays a crucial role in a spectrum of related military and civil applications, such as naval defense and security, traffic surveillance, maritime rescue, protection against illegal fisheries, anti-smuggling efforts, oil discharge control, and sea pollution monitoring, for which automatic ship detection and ship recognition are important to the protection of coastlines and exploration of the vast and rich marine resources. Classification algorithms using color, texture and local shape feature for ship detection were introduced in [24,25] Each of these methods essentially includes an improvement in either preprocessing or classification, to achieve better performance. Han [31] proposed a method of multi-class geospatial target detection by the integration of visual saliency modeling and the discriminative learning of sparse coding. Transform) [45] were proposed to process multi-channel features of color images These models have good performances in target edge detection, whereas low integrity in targets, especially for large targets. We have improved the existing models and further constructed a practical combined saliency model, which integrates multi-frequency information using self-adaptive weights based on Entropy information It is effective in identifying both large and small ships and suppressing interference from complex backgrounds. Provided are a quantitative comparison and an evaluation
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