Abstract
Reliably ship detection in optical satellite images has a wide application in both military and civil fields. However, this problem is very difficult in complex backgrounds, such as waves, clouds, and small islands. Aiming at these issues, this paper explores an automatic and robust model for ship detection in large-scale optical satellite images, which relies on detecting statistical signatures of ship targets, in terms of biologically-inspired visual features. This model first selects salient candidate regions across large-scale images by using a mechanism based on biologically-inspired visual features, combined with visual attention model with local binary pattern (CVLBP). Different from traditional studies, the proposed algorithm is high-speed and helpful to focus on the suspected ship areas avoiding the separation step of land and sea. Largearea images are cut into small image chips and analyzed in two complementary ways: Sparse saliency using visual attention model and detail signatures using LBP features, thus accordant with sparseness of ship distribution on images. Then these features are employed to classify each chip as containing ship targets or not, using a support vector machine (SVM). After getting the suspicious areas, there are still some false alarms such as microwaves and small ribbon clouds, thus simple shape and texture analysis are adopted to distinguish between ships and nonships in suspicious areas. Experimental results show the proposed method is insensitive to waves, clouds, illumination and ship size.
Highlights
1.1 BackgroundShip detection of remote sensing images (RS images) is very important in maritime rescue, fishing vessel monitoring, immigration control, the defense of territory, naval battle and so on
As ships are typically constructed from large flat metal sheets and are usually radar bright and detectable in synthetic aperture radar (SAR) imagery, and due to SAR’s ability to work in all-weather conditions and all-time situations, so much work has been done on SAR images in ship detection
It is noticed that so many satellites give numberless data in a very short time, so it can apply to real time ship detection in the daytime
Summary
Ship detection of remote sensing images (RS images) is very important in maritime rescue, fishing vessel monitoring, immigration control, the defense of territory, naval battle and so on. As ships are typically constructed from large flat metal sheets and are usually radar bright and detectable in synthetic aperture radar (SAR) imagery, and due to SAR’s ability to work in all-weather conditions and all-time situations, so much work has been done on SAR images in ship detection. With rapid development of the sensor technology, highly resolution optical satellite images can provide more detailed and interpreted characteristics to support automated and real-time ship detecting. Compared with SAR imagery, optical satellite images are more visible they can provide more detailed and interpreted characteristics to help human interpretation. The problem of ship target detection is a great challenge in the real optical satellite images: 1) Complex background such as waves, clouds, and islands leads to high loss and false alarms in ship detection. The problem of ship target detection is a great challenge in the real optical satellite images: 1) Complex background such as waves, clouds, and islands leads to high loss and false alarms in ship detection. 2) M any detection approaches often face a serious dilemma, as no robust feature set or a good model can be defined for the large interclass variability among diverse kind of ship targets
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